Bias and Ethical Concerns in Machine Learning

The field of Artificial Intelligence (AI) has advanced quickly in recent years. While artificial intelligence (AI) was merely a theory ten years ago and had few practical uses, it is now one of the most rapidly evolving technologies and is being widely adopted. Artificial intelligence (AI) finds use in a wide range of fields, including product recommendations for shopping carts and complicated data analysis across numerous sources for trading and investing decisions.

Due to the technology’s quick development, ethical, privacy, and security concerns have surfaced in AI, but they haven’t always gotten the attention they need. The fundamental cause for concern with AI systems is prejudice. Because bias has the potential to unintentionally distort AI output in favor of particular data sets, businesses utilizing AI systems must recognize how bias may enter their systems and implement suitable internal controls to mitigate the issue.

What is Bias in AI?

When two data sets are not viewed as equal in AI, it is called Bias. This can be because of preconceived notions ingrained in the training data or biased assumptions made during the construction of the AI algorithm. Current instances of prejudice include:

• An AI-based recruiting tool that appeared biased against women had to be discontinued by a major technology firm.
• A well-known software company was forced to apologize after its AI-powered Twitter account began to post offensive messages.
• A well-known tech company was forced to discontinue its facial recognition software due to discrimination against specific ethnic groups.
• A well-known social media company issued an apology for employing an algorithm to automatically trim images, which showed bigotry by favoring White faces over those of people of color.

Furthermore, the Artificial Intelligence Index Report 2022 5 states that images of Black people were incorrectly categorized as nonhuman in contrastive language-image pretraining (CLIP) experiments at a rate that was more than twice that of any other race. In tests described in the paper from the previous year, Black speakers—especially Black men—were misinterpreted by AI systems twice as frequently as White speakers.

How AI Systems Become Biased?

After testing, the AI program generates a result by processing real-world data using the reasoning it has learnt from the test data. The AI program examines the input from every outcome as its logic develops to better manage the subsequent real-world data scenario. This process allows the machine to learn and adapt over time.

The two primary avenues via which bias enters the AI process are data input and algorithm design. From the perspective of an organization, the contributing elements can be divided into two major groups: internal and external.

Other Factors

Although they are outside of the organization’s control, external influences can have an impact on the AI development process. Biased third-party AI systems, skewed real-world data, and a dearth of comprehensive guidelines or frameworks for bias discovery are examples of external variables.

Biased Real-World Data

The bias that exists in humans is passed to the AI system since it uses real-world data to teach itself when the test data used to train the AI algorithm comes from human-created and real-world examples. Not all population groupings may have fair scenarios included in the real-world data. For instance, the real-world data may overrepresent some ethnic groups, which could distort the AI system’s conclusions.

Absence of Comprehensive Instructions or Models for Identifying Biases

A number of nations have started to regulate AI systems. Numerous international organizations and professional associations have created their own AI frameworks. These frameworks, however, are still in their infancy and only offer broad guidelines and objectives. Customizing them to develop workable policies and guidelines for an AI system that is unique to an enterprise might be challenging at times.
For instance, the recently announced AI Act by the European Union offers some guidelines on how to deal with bias in data for high-risk AI systems. On the other hand, a complicated AI system can also require a few particular bias detection and corrective rules, such establishing fairness and providing AI auditability.

Encourage an Ethics-Based Culture

The intricacy of the tasks that AI solutions are meant to accomplish determines how different they are from one another. It may not always be possible to describe precise procedures for bias identification in a timely manner. Thus, as part of the AI development process, firms should encourage a culture of ethics and social responsibility. Encourage teams to actively search for bias in AI systems by holding frequent training sessions on diversity, equity, inclusion, and ethics; setting up key performance indicators (KPIs); and rewarding staff for reducing bias.

Encourage Diversity

Diversity should be prioritized across the entire organization and not just by the teams that need it to reduce bias. Diverse teams working on AI development ensure that different viewpoints will impact data analytics and AI coding processing, which lowers the requirement for bias avoidance. Including people with a range of traits, including gender, ethnicity, sexual orientation, and age, is essential for creating diverse teams.

Controls at the Process Level

Without suitable process-level controls, entity-level controls might not be enough to mitigate the risk of bias. Determining what constitutes fairness in processing and results is one of the trickiest issues in the development of an AI system. An artificial intelligence system is built to decide depending on specific criteria. A certain amount of weight should be assigned to variables that are crucial for producing correct results. The elements that contribute to equitable decision-making must be defined in a precise and measurable manner. While credit services can be prejudiced as well, an AI loan-approving system that bases choices on income tax return statements and credit scores, for instance, might be seen as more equitable.

Prepare a Balanced Data Set

The AI system’s training data must be carefully examined. Important things to think about when creating a balanced data set are as follows:

• Gender and ethnicity are examples of sensitive data features, together with any relevant connections.
• In terms of item count, the data are typical of all population groupings.
• The right data-labeling techniques are applied.
• To balance the data collection, various weights are applied to different data components.
• Before usage, data sets and gathering techniques undergo an independent evaluation to check for bias.

Conduct Regular Assessments

There can be blind spots when it comes to spotting bias, even with a thorough examination of training data sets and AI programming logic. It’s crucial to regularly check AI system outputs against fairness definitions to make sure bias doesn’t persist if it already exists or develops in the future. For any AI system, there can be a defined acceptable error threshold. Certain high-risk and sensitive AI systems ought to have zero mistake tolerance.

Conclusion

In terms of bias risk, AI systems are not created equal. An AI system that makes product recommendations for a shopping cart, for instance, is less risky than one that decides whether to approve a loan application for a specific person. Different restrictions may be needed to properly address the issue of bias creeping in, depending on the type of the AI system being used. AI systems also carry additional risks, including those related to data privacy, accuracy, and security of AI models. The landscape of technology is evolving quickly and will only become more intricate. Organizations and audit professionals should keep abreast of new advances in emerging technologies.