Businesses, governments, and other organizations widely employ Artificial Intelligence (AI) algorithms. Decisions, once undertaken by humans, are now conducted by algorithms, mostly through ML and AI powered by big data. Incidents of bias and unfairness in various real-world AI applications have led to an ever-increasing public concern about the impact of AI in our lives. If such issues are not carefully tackled, AI-based decision-making may underperform and cause significant societal harm. NoBIAS aims to be the answer in this respect. To achieve this objective, our challenges stem from the AI-based decision-making process, which at a high level involves the following phases: data collection, AI algorithms, and results.
At each step in this process, biases may arise, which need to be accounted for and countered in order to produce business benefits while addressing related legal and ethical concerns. In particular, the three core challenges are: (C1) Data can be biased; (C2) Algorithms can be biased; (C3) Results can be biased.
Research, development and training of early-stage researchers (ESRs) in NoBIAS is organized around the AI-based decision-making pipeline and the identified C1-C3 core challenges and the corresponding O1-O3 objectives (as listed next) to ensure that crucial skills for AI-based decision making in industry and society are acquired and are well-aligned with business value creation.
Objective 1: Understanding bias in data. The quality of the data provided as input to AI decision-making processes strongly influences the results. Understanding why and how bias is manifested in data is of paramount importance. In this regard, NoBIAS has developed a comprehensive view of bias generation within sociotechnical systems, how design and development choices impact representations, formal methods for bias detection, and documenting bias through ontologies.
Objective 2: Mitigating bias in algorithms. To account for bias in AI, we can improve the bias-related quality of the data, or we can introduce extra constraints/costs in the utility measure of the model to “enforce” fairness. The former approach is independent of the algorithm, whereas the latter depends on the algorithm per se. In the context of NoBIAS, we aim to tackle both model-independent and model-dependent challenges as well as connect them with legal issues and contexts. In this regard, NoBIAS has developed both model-dependent and model-independent methods of mitigating bias.
Objective 3: Accounting for bias in results. The results of AI-based decision-making systems might be biased, even if the data has been corrected for bias and even if the algorithms have been modified to account for bias. Moreover, new sources of biases are introduced by the interpretation of the results and application context when continuous model outputs are converted into binary decisions or when concept drift arises over time. In this regard, NoBIAS has developed methods of explaining black-box and white-box decision models, and methods for time-dependent monitoring and mitigation of biases in AI systems.
