Bias detection in AI models

Artificial intelligence (AI) and machine learning (ML) models are human creations, built and trained by individuals. From the very inception of the crucial data collection process to the final deployment of models, human influence penetrates through every stage. Our inherent biases, shaped by personal and professional experiences, can inadvertently infiltrate the data collection and modeling stages, resulting in AI systems that reflect human biases in their outputs. Such biases can have profound repercussions, particularly when AI models are employed in decision-making across various industries, impacting the lives of countless individuals. Biased results can perpetuate inequalities, favoring certain demographics while discriminating against others. As custodians of ethical AI development, it is important that we recognize and mitigate these biases within our models.

Issues with bias: some examples

The manifestation of bias in AI models is very diverse. When an AI model consistently produces biased results—whether in favor of or against certain categories or segments—it signals errors that were introduced during the development process. Let me substantiate this with a few examples: For instance, a model favoring individuals from specific backgrounds or income brackets for approved loans, demonstrates bias. Similarly, the denial of credit to small businesses due to a model's preference for larger or big-sized firms highlights skewed decision-making. Under-representation of minority groups in training data can also lead to biased outcomes, as the model may overlook these groups altogether in its outputs due to insufficient data. Gender-based divisions in professional roles represent serious biases that must be rigorously excluded from our work environments, demanding utmost vigilance and careful scrutiny of our model results. Biased employment decisions, such as rejecting qualified and skilled candidates based on favoring certain educational institutions is another example that underscore the detrimental impact of bias in AI-driven results. Models exhibiting such results, if left unchecked, can lead to systemic disparities in the very near future!

Why is this important in the Indian context?

It is evident that biases become more pronounced as the diversity of classes within our data increases. India stands as a shining example of diversity, boasting a vibrant mosaic of cultures, languages, castes, religions, and socio-economic backgrounds. In a multifaceted society like ours, ensuring the impartial deployment of AI models becomes paramount. With AI now permeating and establishing itself as a powerful tool in various sectors in India, from healthcare and fintech to education and governance, it becomes important to identify and rectify biases before entrusting the results of these models with decision-making. If we are aiming to promote a future that is fair and just, we cannot accept AI models that perpetuate any form of inequality.

Some common AI biases to know about:

Sample Bias: This bias occurs when an AI model is trained on data that is not representative of the diversity of the actual population, leading to unfair outcomes. It can manifest as racial, gender, or socioeconomic biases, where the AI's decisions favor one group over others due to the predominance of that group in the training data. A similar bias, called the Selection Bias (sometimes even used interchangeably with sample bias) occurs when the data is already biased when it’s selected for the model.

Algorithmic Bias: This happens when the algorithms that underpin AI models favor certain outcomes, even if the data is balanced. It might be the results of assumptions made during the model’s design that systematically disadvantage certain groups.

Confirmation bias: This type of bias comes from the developers themselves. AI model creators may keep training and fine-tuning their models till they conform with the subjective outlook of the developer, overlooking any contrary evidence or perspectives.

Mitigating bias and concluding remarks

As is evident from the discussion above, diversity in training data is extremely crucial. The training data should be representative of all groups and cover different demographics to reduce the risk of bias. Moreover, the larger the volume utilized to train a model, the better, as it increases the likelihood of adequately representing all classes or groups.

Employing AI ethics frameworks, like those proposed by NITI Aayog can guide developers in creating more equitable AI systems. India can also learn from international experiences. Initiatives like the European Union's General Data Protection Regulation (GDPR) offer valuable lessons on regulating AI to protect individual rights, including guidelines to prevent AI bias.

Major tech giants globally have also developed tools to detect bias within AI models. Among them are industry leaders like IBM (with AI Fairness 360), Microsoft (featuring the Fairlearn toolkit), and Google (boasting the What-If tool). In India, private sector enterprises are actively engaged in crafting solutions tailored to the Indian market and its socio-economic landscape.

In our quest for a just society, it's important to address biases in the modern predictive landscape to ensure that the strong potential of AI as a technology for everyone's benefit remains unimpeded.