That is ordinarily odd, seeing as humans build the machines, but it is what it is. Humans built a machine that could reach conclusions independently, and now humans are trying to understand the machine’s thought process.

Currently, machines are being relied upon more and more in our society. Algorithms make decisions about a wide array of subjects, from law to e-commerce platforms to media.  Some programs backed by Artificial intelligence have attained the performance levels of human experts.

Even though humans created it, it is often the case that even humans are hard-pressed to understand how the algorithms come to their conclusions, in the now comical case of the amazon pricing war over Peter Lawrence’s The making of a fly. It was worked out that once a day, one seller (profnath) set its price to be 0.9983 times the price of the copy offered by another seller (bordeebook). Meanwhile, the prices of bordeebook were set to rise at 1.270589 times the price shown by profnath. The book about flies peaked at $23,698,655.93 before the algorithm was assessed. We must understand just how these machines come to their conclusions.

To explain our daily lives, we rely on rich and expressive vocabulary: we use examples and counterexamples, create rules and prototypes, and highlight important characteristics that are present and absent. When interacting with algorithmic decisions, users will expect and demand the same level of expressiveness from AI. This is where explainable Artificial Intelligence comes in.

Explainable Artificial Intelligence

Explainable artificial intelligence (XAI) is a set of processes and methods that enables human users to comprehend and trust the results and output created by machine learning algorithms. It is a relatively new concept focused on helping humans interpret the output of sophisticated machine learning models.

As AI becomes more advanced, humans are challenged to comprehend and retrace how the algorithm produces results. The whole calculation process is turned into what is commonly referred to as a “black box” that is impossible to interpret. 

These black-box models are created directly from the data. And, not even the engineers or data scientists who make the algorithm can understand or explain what is happening inside them or how the AI algorithm arrived at a specific result.

Several experts have simplified Explainable Artificial Intelligence:

“The term ‘explainable AI’ or ‘interpretable AI’ refers to humans being able to easily comprehend through dynamically generated graphs or textual descriptions the path artificial intelligence technology took to make a decision.” –Keith Collins, executive vice president, and CIO, SAS.

 “Explainable AI is where we can interpret the outcomes of AI while being able to traverse back, from outcomes to the inputs, on the path the AI took to arrive at the results.” –Phani Nagarjuna, chief analytics officer, Sutherland.

“Explainable AI in simple terms means AI that is transparent in its operations so that human users will be able to understand and trust decisions. Organizations must ask the question – can you explain how your AI generated that specific insight or decision?” –Matt Sanchez, founder, and CTO of CognitiveScale.

But why exactly is explainable AI important?

As critical as they are to the success of our society, AI systems are notorious for their ‘black-box’ nature, leaving many users without visibility into how or why decisions have been made.

Heena Purohit, senior product manager at IBM Watson IoT, notes that AI and machine learning already do a great job processing vast amounts of data in an often complex fashion. But the goal of AI and ML, Purohit says, is to help people be more productive and make smarter, faster decisions – which is much more complicated if people have no idea why they’re making those decisions.

Explainable AI is, in a sense, about getting people to trust and buy into these new systems and how they’re changing the way we work.

“As the purpose of the AI is to help humans make enhanced decisions, the business realizes the true value of the AI solution when the user changes his behavior or takes action based on the AI output [or] prediction,” Purohit says. “However, to get a user to change his behavior, he will have to trust the system’s suggestions. This trust is built when users can feel empowered and know how the AI system came up with the recommendation [or] output.”

There must be the assurance that business outcomes left in the hands of AI are understandable and auditable. In many industries, explainability is often a regulatory requirement for companies employing such models.

Conclusion

With the advent of XAI, we might be one step closer to making machines accountable for their actions in the same manner that humans are.

​​MindsDB believes that any ML generated prediction used to support the decision-making process must also answer three questions:

• Why can I trust the prediction?
• Why did the model provide this prediction?
• How can I make these predictions more reliable?

Without explanations behind an AI model’s internal functionalities and its decisions, there is a risk that the model would not be considered trustworthy or legitimate. XAI provides the needed understandability and transparency to enable greater trust toward AI-based solutions.

If critical decisions are to be left in the hands of machines, it is only sensible that humans understand how these machines come to the conclusions that they come to. Much like a judge in the courtroom has to provide the laws and precedents that enabled them to go to the decisions they did, or a mathematician is required to show the work that got them the final result, so is explainable AI.

However, it is one thing to gather information and another thing entirely to leverage this data for the benefit of an organization. This ability to collect data, find its value, monetize the data, and use it to keep ahead of the competition, among other factors, is the reason why a chief data officer’s role is one that companies take very seriously today.

For many data professionals, the position of the Chief Data Officer is the pinnacle of their career. Two decades ago, the job didn’t even exist. As of 2018, a survey by Forbes showed that 67.9% of companies had appointed a Chief Data Officer. Researchers at Gartner estimated that 9 in 10 companies would have a Chief Data Officer by the end of 2019.

Who is a Chief Data Officer?

A Chief Data Officer in an organization whose job is to keep a sharp eye out for a range of possible alternatives shown by available data that can positively impact the organization they represent. Chief Data Officers are senior executives responsible for the utilization and governance of data across the organization. 

It is a CDO’s job to:

• Ensure that the company’s data is of top-notch quality,
• Initiate forward-thinking, innovative ways for data to be used to differentiate the company from competitors in the market,
• Devise analytic strategies that will bring about optimum utilization of the available data to analyze business outcomes,
• Use the available data to understand patterns and trends, such as how decisions made by the company affect consumer behavior and how they can create more desired results.
• Protect the data that the company gathers.

The Path Towards Becoming a Chief Data Officer

While the position is relatively new, it has rapidly become sensitive, requiring a variable mix of hard and soft skills to succeed. This article covers just what you need if the office of a Chief Data Officer is your destination.

Education

While it is not a requirement to get an IT Related degree, most firms require a Ph.D. in a data-related field like management information systems, statistics, data science, and computer science.

According to research by Gartner, only 9 percent of CDOs had a prior position in a traditional IT department. You don’t necessarily need an IT degree, but you need adequate knowledge of the data sciences. Some companies will accept equivalent experience in place of this education.

Skills

Because the Chief Data Officer is a part of a company’s executive team, you must have 5-10 years of experience in a senior management position, strong leadership and communication skills, project management skills, and effective interpersonal skills, to mention a few. 

Because this role sits at an intersection between IT and business, the CDO will need a broader range of skills than a regular executive. A clear understanding of interpreting statistics and communicating them in a business sense is essential to succeeding in this role.

A senior executive, the chief data officer, should motivate, inspire, and align the entire team behind a shared goal. You will not be working alone, and for your team to be effective, you need to have the capacity to motivate them to do their best.

Practical knowledge of data security is imperative for this role. In countries like the UK, a data protection officer is recommended for each organization. The Chief Data Officer can handle this role. It is the CDO’s job to know how to dispense relevant data to the people who need it in the organization.

The Chief Data Officer needs to have superior IT skills to succeed at this office. An appreciation of what technology can offer is critical to the results a CDO should aim for. Results like the implementation of technology improvements for the benefit of the company.

The chief mistake to avoid on your path to a Chief Data Officer role in the future is focusing solely on the technical side and not enough on the business and management end. Too much weight on the technical end will tilt the balance towards tools, and too little will have the CDO unable to pass his vision across.

A high level of vision is required to carry out the functions required of this office. The CDOs’ role is to ask the right questions and determine the data and strategies needed to answer those questions. A high level of understanding of both data strategy and the particular business of the organization is required.

Experience

If you’re looking to be a Chief Data Officer, you’re looking to be in a top management position and an information-analyzing role. You will find that it is essential to have not less than ten years of experience as a senior data science professional combined with the knowledge of leading a team.

Conclusion

A clear path to the office of the Chief Data officer of an organization could start with a data analyst, data scientist, data engineer, or data warehouse engineer. A promotion would lead to a senior data analyst, senior warehouse engineer, senior data scientist, or senior data engineer. 

Your next step would be head of analytics or head of data sciences, after which you get promoted to director of analytics or director of data science.  It is from one of these offices that you may be appointed a chief data officer. Some companies prefer to hire an executive from another field; however, for the role of the Chief Data Officer, experience counts even if you are looking to change organizations.

Larger firms have specialist data engineers for data engineering. Still, many smaller ones delegate this task to their data scientists, making it mandatory for them to possess a good number of the skillsets characteristic of data engineers. 

Data engineers are responsible for processing raw data. They work behind the scenes, building infrastructure to fine-tune data sets. They are also responsible for processing data on a large scale and the maintenance and monitoring of systems. They have the tasks of acquiring datasets that align with business needs, building, testing, and maintaining the database’s pipeline architectures. Listed below are some of the data engineering skillsets:

• Coding: Data engineers must (at least on a fundamental level) comprehend data structures and algorithms. A mastery of coding languages such as SQL, Python, NoSQL, Scala, Java, and R are vital to this role. They must be learned and practiced till the coder develops a level of proficiency.
• Distributed systems: These include software engineering and architect skills (e.g. Hadoop, Hive, etc.).
• Relational and Non-relational databases: Sufficient knowledge of databases and how they work is vital to an aspiring data engineer.
• ETL systems: Extract, transform and load refers to relocating data from databases into a data warehouse. A data engineer must use ETL tools such as Alooma, Xplenty, Talend, etc.
• Scripting and Automation: A data engineer must write scripts that make it possible to automate repetitive tasks due to the amount of information collected.
• Machine learning: This is present in most areas of data science, and as such, data scientists should have no problem with the basic concepts of machine learning.
• Analytics: This is typically the realm of data scientists but is required of data engineers. It involves the grasp of various mathematical, statistical, and probabilistic principles needed for data manipulation.
• Big data tools: The evolution of technology means a data engineer must harness big data tools like MongoDB, Hadoop, Kafka, and scala.
• Cloud computing: The increasing reliance of companies on cloud storage and computing means these would have to be understood by data engineers and data scientists intending to be capable in that field.
• Data security: Many companies have specialized teams for data security, but where these aren’t available, a data scientist with the required skills can fill in, helping to prevent theft and loss of data.

Having considered who a data engineer is and the skillsets required for data engineering, a review of data scientists and their skillset is necessary to connect the parallels between these two related yet different fields.

Data Scientist

Data science simply refers to using that big data to identify patterns and make informed business decisions. It requires the use of machine learning algorithms and applied-math general techniques to create prediction models.

A data scientist is often capable of the following:  

• Data Manipulation: Collecting, entering, and removing data.
• Data Maintenance: Data verification, staging, processing, and warehousing data.
• Data Processing: Summarization, modeling, and clustering/classifying data.
• Communicating Results: Creating comprehensive reports based on data provided and making informed decisions based on visualized data.
• Data Analysis: Mining texts, predictive analysis, and exploration/confirmation of data outcomes are integral functions of a data scientist.

According to Data Science Central, in the absence of a data engineer, a data scientist will spend three percent of his time building training sets, sixty percent of it cleaning and organizing data, nineteen percent of it collecting data sets, nine percent mining for data patterns, four percent refining algorithms and five percent on other functions. Cleaning and organizing data sets and managing data sets take up seventy-nine percent of the time. 

While this function is unarguably vital, it is not the unique function of a data scientist but that of a data engineer. The absence of a data engineer means a data scientist not capable of performing these tasks cannot do about four-fifths of his job. It is only in theoretical analysis that a data scientist’s job is clearly defined and separate from that of a data engineer. As stated earlier, except for large firms, most companies do not have data engineers and data scientists wholly separated.

In the early days of data analysis, all data scientists were expected to create the required infrastructure and data pipelines that would allow them to perform their tasks. Still, these didn’t match their skillsets of job expectations and frequently resulted in data modeling not being done appropriately. These complications caused redundancies and inconsistencies in the way data was used. This led to data projects yielding less than optimal values and their predictable failure.

Conclusion

With this overlap between engineering and data science roles, it would be easy to erroneously believe that a data scientist can easily double as a data engineer; however, skill level in some functions highlight fundamental differences between both fields. 

While data scientists are typically well-versed in mathematics and statistics, machine learning, and algorithm techniques, data engineers lean more towards MySQL, NoSQL, SQL, cloud and architecture technologies, etc.

In the words of data scientist Soner Yildirim, a data engineer is a data engineer, but a data scientist should be both a data scientist and a data engineer.

Data Science?

As is already common knowledge, Data Science is a field of study that has become a more relevant and exciting area for exploration. It is a sub-field of general tech and AI that effectively maximizes various algorithms, machine learning tools, and principles to reveal patterns from raw data, applying and appropriating this information to innovative technology.

Tech is the future, and this future, as we now know it rests on the shoulders of revolutionary proactive thinkers, innovators, and professional tech gurus like you. The job of a Senior Data Scientist encompasses a range of similar roles; however, it can be narrowed down to essential supervision and guidance. All those years of hard work as an entry-level data scientist did not occur to keep you under the hammer forever. As a Senior Data Scientist, your roles may often be supervisory.

While we may conclude that a senior data scientist has paid his dues and should indeed be involved with only supervisory roles, being a leader often means taking the fall for errors of less experienced team members. It may also mean that you have to do the heavy lifting on tasking projects, especially if your team is predominantly inexperienced data scientists. Your duties now involve cross-checking and confirming the accuracy or authenticity of projects and jobs executed by your team.

As a Senior Data Scientist, your job detail covers analysis, management, strategizing, providing relevant information, and collaborating with other teams to achieve business goals. Depending on your industry, your job specification may now involve producing and maintaining a valid and working analytic structure that will lead to full-stack scalable data analytics and solutions to tech business problems.

Consequently, senior data scientists may choose to build their expertise to appropriate statistical models and apply statistical capabilities. You should know what tools work best and learn how to use them. You will also be tasked with drafting and editing reports on the junior data science departments’ performance, giving unbiased details on how well they have performed and plans on strategies for analytics.

Managerial Positions?

Moving forward, a senior data scientist may occupy managerial roles, so ample knowledge on how to work with people will be an added advantage in this regard. As a manager, you will supervise the performance of junior data scientists attached to your detail to ensure full implementation and thorough execution of their assigned duties.

You will also be expected to vet the quality of projects done, double-checking to ensure they are in sync with business goals and objectives. As a manager, you’ll need to ensure that the data department constantly works co-operatively to produce innovative ideas and solutions that will ultimately translate to better opportunities and desired results for team members and the business in general.

Strategic roles can be filled in by Senior Data Scientists as well. In this capacity, you are responsible for the calculated formation and production of innovative ideas. You get to work with vast datasets and aim to achieve working, seamless analytical systems devoid of hitches along the line, regardless of whatever platform or application it is run on.

You also initiate and oversee the production of excellent and reliable algorithms that will enable easy implementation of object classification, segmentation, detection, and recognition. You are responsible for strategically positioning your company or business for profitable opportunities using algorithms, mining techniques, and other statistical tools.

You may also look towards converting business goals into prototypes which will ease generating data frameworks that translate to accomplishing set goals as you move forward.
The path after acquiring the status of Senior data scientist is full of possibilities. Depending on your preferences, there isn’t precisely a predetermined path for everyone.

For instance, you may be saddled with the responsibility of updating your team with relevant knowledge on what is obtainable in your field per time. As a result, you cannot afford to let your knowledge bank go obsolete as it could jeopardize chances of copping juicy business deals or stall business growth.

In specific industries, you are expected to keep up with evolving trends, developments, analytic tools/practices, designs, and news, as this could either strategically place you in a position for catapultic progress or leave you behind as more innovative ideas and techniques are plowed into the tech industry daily. As always, you are to play a supervisory role in giving the data science department professional advice.

Understanding Your Duties

The job of a Senior Data Scientist is bordered on collaboration. In this capacity, you work with the junior data analyst team members to work out analytics systems, then oversee it to the point of prototyping and eventual production. When the systems have been developed, you are also expected to work with the senior data science management team responsible for perusal and vetting. Working hand-in-hand with the team, you are tasked with arranging insights to prioritize, making informed decisions, and ensuring that business goals are fully met.

As you have probably deduced, a senior data scientist’s potential roles and duties are intertwined and interdependent. They are not too complex, nor do they significantly differ from the current responsibilities of a senior data scientist in terms of workload.

You have gotten this far in your career through sheer determination, dedication, hard work, and maybe a determined stance against financial lack; you deserve your accolades – every last one. It only gets more accessible and better from this point, now that you are a senior data scientist.

In2015, IBM released a modified version called Analytics Solutions Unified Method for Data Mining/Predictive Analytics (ASUM-DM).

In the space of twenty-five years, the relevance of CRISP-DM in the data mining industry has only become more pronounced and is even more advantageous in 2021. Thanks to the timely modifications applied to the model, it has become an industry favorite for obvious reasons. It has notably solved many pre-existing data mining industry problems and continues to function as a solution to new challenges in the data-mining world.

Data mining as a phenomenon has been a handy tool in the AI and tech world generally. However, some of its challenges lie in its implementation stage, with discovered faults in the data pool. Issues like complex or incomplete data, the data privacy argument, and the distribution of data, among others, could pose a threat to the smooth running of the data-mining process. That is where CRISP-DM comes in. 

CRISP-DM, as a data-mining model, consists of six phases which make the process a seamless one;

• Business understanding: In ensuring conversion to analytic problems, the business requirements must be fully comprehended. This aids ease of strategy in solving issues discovered. Understanding the business requirement is a requisite phase of the CRISP-DM process whose relevance cannot be overemphasized. Solid foundational knowledge in what is needed will help avoid unnecessary waste of energy, time, and resources on a wrong problem statement. This phase could be made possible through steps like; establishing the actual business goals clearly, correctly assessing the situation, weighing the details, determining the data analysis objectives, and producing a viable project plan. 

All of these go into the ‘business understanding’ phase of the CRISP-DM process.

• Data understanding: A spill-over from the business understanding phase, the data understanding phase comprises all the activities that involve scrutiny, assessing, assembly of received data sets which ultimately aid the success of the entire project. Data understanding can be materialized with the aid of a few sub-phases such as the collection of relevant data, detailed description of collected data (including data volume, availability of the data attributes, data types, correlations, range, etc.), proper exploration/query of data and delicately verifying the data.

• Data preparation: The data preparation phase of the CRISP-DM model encompasses all activities involved in producing the final data sets necessary for the modeling phase. Also referred to as ‘data wrangling,’ data preparation entails specific tasks geared towards creating data sets to develop the raw data into the final data sets. For best results, this phase consists of five tasks: data selection, data cleaning, data construction, data integration, and data formatting. Data miners incorporate these tasks into the data preparation stage to eliminate any possibility of inaccurate data or information production.

• Modeling: During the execution of the modeling phase of the CRISP-DM model, the first and most crucial step is the selection of the required modeling technique (confirming specific algorithms to be experimented on), generating the test design, actual building of the model, and then assessing the developed model. The role of the data scientist or miner during this phase is to initiate the modeling process and then interpret achieved results using parameters such as test design, expected success criteria, and general domain knowledge.

• Evaluation: After the modeling phase has been executed, the model is adequately evaluated and assessed. All previous phases are re-run and checked for errors, omissions, and confirmation that the model achieves business goals set at the initial stage of the process. 

During this phase, cross-checking is thoroughly executed to ensure the effective achievement of all business issues, confirming that none has been left out in the overall consideration. 

As is typical with previous phases, the evaluation phase comprises three major tasks: a thorough evaluation of results, properly reviewing the entire process, and ascertaining subsequent steps to be taken. At this stage, the data scientist determines how feasible deployment can be for each obtained result.

• Deployment: This phase involves the actual resourcefulness of the produced model as the entire process is seemingly a waste of time and investment if the product cannot be applied by the clients or target market. 

Complexities lie in this process, especially with the broad range of what may be described as ‘deployment,’ based on the specific requirements. A deployment phase may just include informing the client on usage or involving the actual re-mining of data. 

Whatever the case, the deployment phase involves four major tasks which aid a conclusive result. They are:

1. Plan deployment (everything that entails developing the deployment plan, taking into cognizance user experience).
2. Arrangements for monitoring and maintenance (maintenance tools can be arranged to ensure glitches during the operational phase).
3. Production of a final report and a proper project review.

Although these phases can be sequential, tasks can be performed randomly, especially in urgency and without necessary regard to order, as adjustments can be made by backtracking previous stages and editing actions. 

CRISP-DM could be considered as a data miners’ favorite and not without compelling reasons. It is a pocket-friendly methodology as it employs several processes that ensure the avoidance of costly errors. It is primarily fire-proof. 

CRISP-DM is a model that makes for a unified framework and enables the implementation of project replication and best practices. Now more than ever, this thorough, advanced methodology is of great relevance as the tech world continues to produce innovative ideas and results necessary for a better future. 

The global AI index takes a detailed look at various key indicators to measure the who-is-who in the AI industry and the changes experienced within the industry due to global factors. This review considers 62 nations worldwide by examining the forces of accelerated development in artificial intelligence by considering three areas.

These three areas include:

• Innovation.
• Implementation. 
• Investment.

Key Points from this Global AI Index

AI has become an integral part of drug design and discovery as investments in medical AI represent the most significant private investment for the pandemic-ridden year of 2020. With an approximate figure set at USD 13 billion, AI investment in drug discovery and design remains the most heavily invested sector, over four times the investment in previous years.

Thanks to extensive innovations by the highest-ranked countries in this index, AI systems are now more capable of multimedia composition. Texts, images, and audio composed by AI are almost indistinguishable by humans. It is becoming increasingly challenging to differentiate synthetic or non-synthetic outputs from a human point of view. 

Some countries include the United States, which has perfect talent, infrastructure, research and development, and commercial investment. These ensure that the U.S sits comfortably at number one (1) of this global AI index. Seconded only by China which comes in second in terms of infrastructure, research, and commercial investment.

Other countries that make up the first 6 include the United Kingdom, Canada, South Korea, and Israel. These countries boast at least 60 points in government strategy in terms of investment except for Israel.

China seems to be challenging the U.S for the top spot as the country has overtaken the U.S in AI journal citations. At least 65% of graduating PhDs continued their path within the foray of industry in the U.S. This continues to highlight the growing roles of AI in the industrial sector.

While there’s a notable increase in the influx of PhDs into the industry, there have been concerns about a diversity challenge in the U.S, citing over 45% of new U.S resident Ph.D. holders being white as opposed to a measly 3.2% Hispanic and 2.4% African American representation respectively.

On the bright side, the U.S seems to be enjoying a large talent pool as it holds the number one(1) spot for talent and infrastructure. This may be attributed to the growing willingness of international students among new AI PhDs within the North American continent to stay back in the U.S. 

A whopping 81.8% are said to be staying back in the United States, while only 8.6% have agreed to take jobs outside the United States. One of the major drivers for the insistence of foreign AI PhDs to remain in the U.S is the special infrastructural provisions and the sheer volume of private and government investment in AI.

Perfect scores in infrastructure and commercial investment make the U.S an AI enthusiast’s dreamland.

Technically and Ethically

On a more technical level, surveillance technologies seem to be benefiting extensively from the innovations within the AI industry. These techs have become highly affordable and readily available on request. This has given rise to concerns by AI pundits on the possibility of large-scale video surveillance deployments as significant growth in face recognition and voice identification, among others, have become quite evident over the past few years.

This has essentially given rise to the consistent call for evidently lacking AI benchmarks, and consensus as existing ethics do not qualitatively or normatively state the boundaries of AI technology.

Perhaps, due to these lapses in ethics, AI has garnered the attention of the U.S Congress, with the 116th congress being the most AI-oriented congress in history.

A lot seems to be happening in the U.S with regards to the AI industry. However, let’s take a look at some of the other countries in our list of 62.

Other Countries and AI

South Africa, Tunisia, Morocco, Kenya, Egypt, and Nigeria respectively fall within the last ten countries in the global AI index review, suggesting that Africa remains behind in the race towards AI.

Nigeria comes at number 61 with zero points in government strategy regarding investment and zero points in infrastructure. Pakistan completes the list at number 62, significantly lacking in the areas of the operating environment.

Brazil, Argentina, and Colombia all make a list, respectively, with Brazil scoring an impressive 54.6 in government strategy investments.

Overall, North America, Europe, Asia, and the Middle East are all well represented in the first ten (10) spots of the global AI index review. The first ten countries are as follows:

1. United States of America
2. China
3. United Kingdom
4. Canada
5. South Korea
6. Israel
7. Germany
8. The Netherlands
9. Singapore
10. France

The bottom ten (10) nations include:

1. Turkey
2. South Africa
3. Tunisia
4. Morocco
5. Vietnam
6. Kenya 
7. Sri Lanka
8. Egypt
9. Nigeria
10. Pakistan

A notable pattern is the seeming availability of infrastructure by most countries with inadequate talents to utilize these infrastructures effectively. Also, it turns out that investments in artificial intelligence from commercial sources may be attributed to the exponential growth observed in countries such as the U.S. 

This may, to large extents, explain the gap between the U.S and other countries on the list. Most countries that come close with little to no commercial investments are supported by government strategy investments. These countries include Canada, China, South Korea, Germany, and the United Kingdom, respectively. The U.S also boasts of a 77 point score in terms of government strategy investment, making it the hub of AI investments.

Other notable mentions include the UAE, which has a perfect score of 100 in government strategy investments but remains below the first 20, perhaps due to a severe lack of available talent. This is also the case with Saudi Arabia, which holds a much higher position than the UAE on this list.

The Global AI index points out that one of the significant proponents of AI growth is Commercial investment. Other integral factors include the availability of talent and infrastructure.

Macro View (the big picture)

• The use of AI would inevitably reduce the number of human workers with repetitive tasks as processes get more streamlined and redundancies/dispensable factors are eliminated. An example cited by Nesta is the adoption of an automatic tool for recommending books resulting in human book reviewers losing their jobs.
• AI improves efficiency in terms of the output of human tasks. Costs that might have accrued due to calculations and simulations needed for prototype creation and scenario reproduction can be reduced or altogether avoided. The only workers that would benefit from this are those involved in work that can be enhanced rather than eliminated by AI.
• Higher quality products and services will be achievable using AI, resulting in greater customer satisfaction and increased revenue.
• As much as it is a problem solver, AI poses new ones, especially machine learning, creating new jobs.

The efficiency of AI will result in more significant innovation, which opens up new industries and diversifies existing ones. The above instances go a long way in refuting the prevalent opinion that the advent of AI heralds a shift towards wide-scale unemployment. Still, the fears are not unfounded, especially if firms take a half-hearted route towards AI incorporation, causing job losses but not opening the avenues for creating new ones.

Meso View

This view considers AI’s effect on specific jobs/fields and policies that might check the more negative effects. An example to illustrate this is the use of AI in the agricultural sector; by taking weather and soil constitution into account, the ideal crops to be grown and livestock to be reared can be easily projected. The most efficient machinery can also be known beforehand. 

This knowledge will inevitably lead to many planters, harvesters, and other similarly non-essential personnel being laid off. At the same time, skilled technicians would be engaged to manage machinery and machine learning systems that are to be utilized. 

In this scenario, increased crop yield and optimized management of livestock and processing measures bring the firm profits, but many people have lost their means of livelihood. This indicates that a wage gap could become evident between the qualified beneficiaries of AI (management personnel and skilled technicians) and the unskilled (and very likely uneducated) losers, which would be detrimental to the socioeconomic balance if left unchecked.

AI has been engaged in some health facilities to reduce scheduling errors, reduce the time taken in fulfilling preregistration requirements, etc. This affords medical personnel time to focus on issues more closely related to health that would have been expended on menial pursuits.

In manufacturing-intensive economies like those of Asian countries, AI has contributed to significant growth with projections of a sharper increase as aspects of its application become more fine-tuned. AI in manufacturing will lead to AI taking up more dangerous jobs potentially harmful to humans either by accidents or substance toxicity.

Micro View

This focuses on how organizations and people will be affected by AI. It includes taking AI limitations into account as well as its progress. From an organizational standpoint, there is a reservation that AI would lead to a monopoly in which pioneering firms would wield a disproportionate financial strength, leaving individuals and possibly nations at their mercy. 

Because the prominent drivers of AI (who unsurprisingly are also the significant researchers in the field) are profit-driven institutions, socioeconomic balance is unlikely to rank high on their list of considerations regarding the effects AI would have on a more significant economic scale. This possibility of an economic catastrophe can only be avoided by political elements, particularly governmental bodies and their regulatory arms becoming early players in the AI game with the primary role of acting as referees.

Meta View

This is an exploration of the means through which AI methodologies can be used to examine AI itself. Machine learning tools can be harnessed to investigate the process used by AI to arrive at specific results.

While it is likely that AI would dramatically transform the workspace, there is no concrete evidence that it would be a negative transformation. Job specifications and roles will experience evolution as AI permeates industries. Companies will make successful and failed forays into AI adoption, and an AI economy will have a lot in common with the internet economy we are currently experiencing.

Impact in Economics as a Discipline

Unlike conventional business models with a capital consisting primarily of machines and buildings (physical structures), AI deals with intangibles like data and information whose creation is more complicated and whose security in terms of inability to be compromised is never wholly assured. This estimates potential worth more guesswork than statistic-based at present.

AI will also be instrumental in understanding economics itself and aiding in formulating and analyzing economic theories. Natural sciences involve examination of existential data, then modeling and projecting possible outcomes. Economics, on the other hand, has the added factor of unpredictability/irrationality of human behavior. 

One of AI’s core strengths is sifting through large datasets and identifying even the most subtle patterns that would typically escape previous observation methods. This would be invaluable in recognizing human behavior based on choices made and predicting future decisions to a more accurate degree. The application of AI in economics is still in its most rudimentary stages.

Developed countries have a lower GDP to growth ratio increase due to the aging population and higher wage rate than developing countries. Introducing AI-capable machines would lower this wage rate and negate the aged population barrier. This could result in these developed countries having the economic edge over the developing ones, which will take longer to adopt AI.

Explainable AI refers to Artificial intelligence, which produces comprehensive results assimilable by humans. It is often referred to as the direct opposite of the black box concept in machine learning which projects results that are often unexplainable by even the designers of the AI.

In a nutshell, explainable AI is understandable AI hence its potential application in the financial sector. Its applications may be attributed to its unique feature of decision traceability. Owing mainly to the potentially detrimental effects of mistakes by AI in a volatile industry such as finance, financial institutions have been a little laid back with regards to the implementation of AI to ensure a more effective industry.

Such errors could emanate from the AI black box concept; thus, the growing suggestions within the financial community to apply explainable AI in finance. Explainable AI can show reasoning and errors and give room for using performance improvement and risk mitigation tools.

A typical example of the loopholes of the AI black box concept is Apple’s digital card which was accused by the populace of discriminating against women as the card for reasons unknown offered more credit to men than women.

By applying explainable AI in finance, we can be sure of more transparent outcomes aligned with regulatory requirements. In a nutshell, explainable AI (XAI) explains why specific results were reached and how these outcomes came to be.

How does explainable AI function?

XAI provides a justifiable reason for every outcome. This means that if a client’s loan request is turned down, as is the case when dealing with humans at the bank, the client may request reasons why their application was turned down and suggestions on meeting the minimum requirements for the loan. 

During this query, the XAI will consider several factors. These factors include the operation method of the model, the algorithm being used, and what factors directly affect the results being shown.

Comprehensive explanations

Thanks to these factors, we can explicitly tell if the XAI is functional or defective based on the factors mentioned above’ answers.

Since we are talking about machines for the most part, and most people still attribute the generic ideas of garbage in garbage out to modern-day AI, it is essential to state that the outcomes shown by XAI are entirely explainable.

This means to say that for every loan given or rejected, there must be a meaningful reason given by the system to justify the action undertaken. While the definition of significance in this context is not exactly objective, it is expected that the end-user can comprehend the information provided.

Clear and concise explanations and limited knowledge base

By giving clear and concise explanations, it becomes a lot easier to verify the actions taken by the system. Hence, there’s room for accountability which generally improves trust among users.

Exceptions must exist as well in a bid to curb unrealistic outcomes. This further mitigates the risks of fatal errors as errors are limited within certain acceptable thresholds.

Implementing XAI in Finance

Certain factors must be put in place if the financial sector will actively adopt XAI. Some of these factors include:

Inadequacies in Customer Onboarding: Due to weaknesses in customer onboarding processes and the highly competitive marketplace, financial institutions lose a lot of money. There is room for more extensive checks by implementing explainable AI, and risks are seriously mitigated to ensure less expensive operational costs.

Risk Mitigation: By checking current trends and comparing these trends with past inconsistencies and red flags, fraud and other malpractices are often easier to spot by explainable AI systems in the financial industry.

Better forecasts: Based on set key indicators, explainable AI, if implemented, is sure to make accurate projections about the finances and performance of the financial institution in question.

It also provides a reference point for eventual outcomes to be measured to promote accountability within financial institutions.

Cash Management: A rather esoteric challenge most financial institutions face is cash management. Justifying the right amount of cash to be made available by considering volatile factors like ATM withdrawals, loan demands, seasonal withdrawals, and events can be quite a task. Thankfully explainable AI can project the cash needs based on insights and historical data.

All this talk about Reinforcement Learning and Explainable Reinforcement Learning

Reinforcement learning (RL) provides leeway to any system based solely on its solution strategy. Due to the sheer number of available strategies towards the same goal, a level of analysis is conducted to choose the best possible approach to complete a given task.

Specific solutions for this involve adding transparency to typical RL operations. This is often called explainable reinforcement learning (XRL).

Explainable reinforcement learning (XRL) is fundamentally made up of two possible solutions. These solutions are interpretability and algorithmic transparency. To effectively utilize these solutions, they must be made more comprehensive to ensure better regulatory practices by respective bodies. 

A more comprehensive solution would mean adoption by consumers, which implies a complete and utter understanding of the principles of these systems by all parties.

To better understand the concept of explaining reinforcement learning (XRL), we can look at state representation learning (SRL) which refers to a system of feature learning that mitigates dimensionality of a system in a bid to ensure seamless understanding of complex systems by RL systems.

In a nutshell, state representation learning (SRL) can identify the why and how of certain decisions.

Currently, a more significant percentage of methods used in XRL exist in robotic and image recognition applications. However, this is expected to expand to other areas, especially the financial sector.

This is arguably one of the reasons why it is essential for an explainable AI. AI that provides reasons and solutions is critical since the financial system is a rather sensitive sector. 

Over the years, transparency in algorithms has become a pressure point within the AI industry due to the 2010 flash crash events. Hence, the increasing need for explainable AI especially pertains to sensitive and volatile sectors like the financial sector.

A sub-field of AI in recent tech is Machine Learning (ML), which encompasses identifying, studying patterns, and leverage them. Machine Learning Operations is requisite for Machine Learning to be possible; it is the lubricant factor in the Machine Learning process.

As explained by AI writer Stephen Watts, MLOps is a practice for collaboration and communication between data scientists and operations professionals to help manage production Machine Learning (or deep learning) lifecycle. Complementary, according to AI Multiple’s Cem Dilmegani in a recent blog post, MLOps practices help us standardize and streamline the construction and deployment of machine learning systems, covering the entire lifecycle of a machine learning application from data collection model management. 

MLOps serve to enlighten and modify ML production while recording business profits and upholding binding regulatory policies. MLOps encompasses all engineering activities that aid the smooth running and maintenance of AI models.

Be that as it may, data scientists and programmers have recently lamented some of the bottlenecks in Machine Learning Operations (MLOps) infamous for delaying production processes. Top contenders for the most cumbersome MLOps include – but may not be limited to – Model Monitoring and Model Maintenance and retraining. 

Quoting the Full Stack Deep Learning alumni survey infographic, the Machine Learning lifecycle stages that proved most difficult for programmers and data scientists were those above, with the former recording 60.0% of the population study asserting to its difficulty and 61.1% admitting that the latter was their most challenging stage of the Machine Learning process to get past.

Model Retraining

Model retraining is the ML operation that sees the correction of varying data by retraining the model to be more suitable for the program. Even after the production process is seemingly completed, some errors or inaccuracies may surface, necessitating a do-over involving a new set of data for better results. 

The challenging factor in the Model retraining stage is probably derived from model deployment complications that entail a seemingly endless retraining process. This is owed to the fact that when it has to do with models, degradation is inevitable; therefore, the retraining process is almost always necessary, at least for satisfactory results.

Model Monitoring

On the other hand, Model Monitoring is the stage of Machine Learning that closely observes the overall performance of Machine Learning models for the AI team to recognize flaws and prevent these flaws from sabotaging business opportunities. In simple terms, model monitoring is the stage in the ML lifecycle where the model is monitored for potential errors and eliminating (or adding) causal (or relevant) data, ensuring that the model is updated and improved to suit different scenarios and tackle them self-sufficiently. 

Model Monitoring practices are essential for preparing against failure of the algorithm and generally serve as a sort of insurance for the machine learning model against obsolescence and potential loss.

Both Model Monitoring and Model retraining have been identified as bottleneck processes in Machine Learning, regardless of their collective value. For Model Monitoring, the most probable reasons for its popular interpretation as cumbersome would be its time-consuming nature and general lack of knowledge on adequately executing the monitoring process. 

Most AI teams in model production processes are eager to release their models into the trade market, and the monitoring practices involved in ML are infamous for stalling launch programs. Tech companies are driven by two fundamental factors; the desire to meet human needs through revolutionary technology and make money. Model Monitoring significantly slows down the process and is not exactly the most anticipated stage of ML model deployment.

The other possible reason for the general dislike of this stage is the unfortunate lack of tech know-how on the appropriate or applicable ML model monitoring practice to be implemented. 

Monitoring ML systems certainly require modalities different from the popular monitoring processes relevant to regular software. For whatever reason, many data scientists and programmers are yet to grasp the knowledge needed for proper Model Monitoring fully. Therefore, this makes the process unappealing to most professionals. 

It may be likened to going out to do your chores and getting to the point where you have to do your most hated task. You wish you do not have to – you seek shortcuts to avoid doing it, but you know you HAVE to; it is inevitable. Whether appealing or not, Model Monitoring is necessary to enable early detection of possible flaws and provide a fix to them. It is all for the greater good.

In the case of Model maintenance and retraining, its’ financial strain on AI companies and some of its obsolete, stressful retraining tasks and practices represent the primary bottleneck. 

Constantly ‘updating’ ML models can be costly for tech companies who unavoidably have to spend a lot of funds on human resources, computational costs, and actual implementation. 

Companies do not particularly relish the thought of constantly investing finances into maintaining skilled human resources whose job it is to retain and retrain ML models. Running costs on computation is also a cause for worry for most AI companies involved in ML model deployment. 

It also does not help that some of the Model retraining practices are painfully cumbersome and difficult to execute. Reasons aside, model maintenance is utterly relevant for a balanced ML model production as these practices ensure that the models are in sync with what is obtainable in our ever-changing world.

It, therefore, goes without saying that Data Science is at the heart of the future of technology. As Hemant Sharma succinctly puts it, Data Science as a field of study is the effective use of algorithms and machine learning principles to uncover patterns from raw data. Basically, Data Science is the meticulous study of unearthed data secrets, suggesting and deciding the most economical ways to apply and appropriate these data. 

Appropriation can be achieved through prediction (using predictive causal analytics) and then through prescription (with the help of prescriptive analytics).

Because of the apparent relevance of data science – especially in our ever-changing world – the importance of exploring the field of study that is data science cannot be overemphasized – well, at least studying it up to the basic BSc level. However, the recent trend in the tech academia world is the craze to get degrees, mainly Ph.D. degrees in Data Science. 

What’s your purpose for getting a Ph.D.?

This is quite understandable; most people do this intending to use the degree to boost their chances at a better job with a data science team or research group. Sadly though, some other people desire a Ph.D. degree but without a structured plan or compelling reason. 

Towing the latter path is risky and pointless, considering that much resourceful and beneficial work is already being done even with teams of data scientists who are not exactly armed with Ph.D. degrees. For those who fall in this category, their primary drive to study data science at a Ph.D. level is solely to solve world problems by finding answers to pre-existing problems and discovering suitable solutions to these problems – Data Science Ph.D. degree holder or not.

So many of these scientists were not professionals when they started their careers as data scientists/analysts. Some of them had to transfer from slightly variant fields of study like neuroscience and biology. Still, they were initially fueled by the longing to effect positive change in the AI and tech world, and there have been recorded successes for sure. 

One could therefore insinuate that passion, skills, and experience supersede certifications when it comes to the field of data science, as a person with a BSc degree in any science-related field who is willing to learn and work towards change is more likely to add more value than a person who is driven to get a Ph.D. for unclear reasons.

These points beg the question; Is having a Ph.D. in Data Science even worth it anymore? Is it the pocket-friendly decision to make, bearing in mind the uncertainty of job security and career success even after bagging the Ph.D. degree? 

Is it possible to balance the weight of pursuing a Ph.D. and the likely accompanying mental health challenges and complications usually recorded among Ph.D. degree students and graduates?

Mental Health of Ph.D. Students: A Disturbing Reality

Getting a degree is a lot of mental and physical work, with many financial burdens attached to it (although most people are consoled with the anticipation of the eventual benefits). Getting a Ph.D. is even more burdensome. Loans are one option to solve the financial part of the burden. Still, even loans do not make the process easier, considering that the thought of debt and inability to pay up may even endanger one’s mental health conditions, especially when the time for payment is fast approaching with no plan or solution to generate the funds. 

Spending so much to get a Ph.D. for securing a job in Data Science could potentially be risky because, as earlier pointed out, skills and passion will most likely be chosen over a mere degree or certification, even when it is a Ph.D. So then, what happens to the person who has invested so much money into obtaining the Ph.D. but is unsure of a successful career in Data Science?

This dilemma could pose a threat to the mental health of such persons, coupled with the continued issue of depression in Ph.D. students, becoming very rampant in recent times. In 2017, a study carried out on 3500 students in Belgium showed that one in two Ph.D. students suffers from depression or other mental health problems. This may stem from overwhelming workload pressure, socio-educational-related stress, or some other challenge. 

Regardless, getting a Ph.D. in data science does not guarantee anything concrete, as records show that even when a person successfully bags the Ph.D. without mental health issues, they are still at risk of having psychological problems later. This could initially be overlooked and ignored as unimportant. However, the ripple effect will become more and more evident in the increased number of subpar research work and results generated in the Data Science and AI industry, even with the concurrent increase rate in Ph.D. degree-holding ‘specialists’ plowed into the industry. 

It becomes irrelevant to all the parties involved whether scientists assigned to specific projects are Ph.D. degree holders or not if the worth of their work is compromised due to unstable mental and psychological problems.

Ultimately, advanced educational knowledge of data science, especially at the Ph.D. level, is an admirable feat and would be instrumental for practical AI and tech industry changes. However, the misconception that a Ph.D. guarantees career success should be addressed and corrected, as it would save many potential aspirants wasted time, money, and effort. They must understand that the essential tool for any form of progressive change is the zeal to create the change.

Here is a good list of Ph.D. programs in Data Science: https://www.discoverdatascience.org/programs/data-science-phd