The potential benefits of artificial intelligence (AI) are almost unquantifiable, particularly in the context of healthcare, transportation and education among hundreds of other sectors and fields. But a balance must be struck between harnessing the technology’s power and mitigating its inherent risks.

One major area of concern is in the potential bias of AI systems, which – if not properly mitigated – can lead to ineffective, unfair and discriminatory outcomes.

In this blog post, we provide an overview of AI bias before outlining some of the very best resources and services for those looking to address this significant issue from a technical standpoint.

Why bias occurs and why we need to mitigate it

AI bias can occur at any stage of the AI development lifecycle, from data collection to model training to deployment. It can be caused by a variety of factors, such as unbalanced data sets, algorithmic flaws, and human bias.

Algorithmic bias can significantly impact people's lives. An AI system that is biased against certain racial or ethnic groups, for example, could deny them loans or jobs. There is, unfortunately, a growing list of real-world examples of how bias has led to real-world harm.

To address concerns about algorithmic harms, several methods are commonly employed. Data cleaning and balancing, for example, involves removing or adjusting biased data points to make sure the dataset adequately represents the intended population. Additionally, there is an emerging focus on algorithmic fairness, which entails developing algorithms that are less prone to bias, using fairness metrics to evaluate their performance.

Complementing these technical strategies is the crucial role of human oversight is essential, wherein experts review AI systems and their outputs, aiming to ensure that any lurking biases are promptly identified and rectified.

Learn how to mitigate bias with Holistic AI and the Alan Turing Institute

As world leaders in AI Governance, Risk, and Compliance, Holistic AI supply two academic courses in collaboration with the Alan Turing Insitute which provide a technical overview of how to mitigate bias in algorithmic systems.

The first, Assessing and Mitigating Bias and Discrimination in AI, is a comprehensive exploration of bias in AI systems, equipping learners with both the foundational knowledge and practical tools to identify, understand, and address bias in machine learning, catering to both beginners and those with coding experience in Python.

The second, Assessing and Mitigating Bias and Discrimination in AI: Beyond Binary Classification, gives technical professionals the tools and strategies to address fairness concerns, expanding on foundational concepts and diving deeper into multiclass classification, regression, recommender systems, and clustering, while integrating robustness, privacy, and explainability considerations.

Together, these courses underscore Holistic AI and the Turing Institute's shared commitment to fostering both technological prowess and ethical responsibility in the realm of AI.

Open-source tools and resource to identify, understand and mitigate bias

As awareness of the need to tackle bias has grown, so too has the repository of open-source tools available to streamline this process.

Some of the most effective tools include:

‍Holistic AI Library

‍This library is designed to assess and enhance the trustworthiness of AI systems.

It provides AI researchers and practitioners with techniques to measure and mitigate bias in various tasks. Its long-term objective is to present methods for addressing AI risks across five key areas: Bias, Efficacy, Robustness, Privacy, and Explainability.

This approach ensures a comprehensive evaluation of AI systems. Additionally, the aim of the Holistic AI Library is to minimise risks associated with AI and data projects by introducing risk mitigation roadmaps, which are guides designed to help users navigate and counteract prevalent AI risks.

IBM AI Fairness 360‍

This is a toolkit that provides a variety of metrics and visualisations for assessing the fairness of AI systems.

It provides AI developers and data scientists with a suite of state-of-the-art tools and metrics to ensure fairness throughout the machine learning pipeline, from data training to prediction.

By incorporating comprehensive metrics and algorithms, the toolkit addresses biases in datasets and models, making it an essential tool for those aiming to uphold fairness in AI systems.

LinkedIn Fairness Toolkit

‍This Scala/Spark library is tailored to assess and address biases in large-scale machine learning processes.

It enables users – often data scientists, engineers and researchers – to measure fairness across datasets and models, pinpointing significant disparities in model performances across varying subgroups.

The library also incorporates post-processing techniques that adjust model scores to ensure equality of opportunity in rankings, without altering the existing model training framework.

You may also want to consider resources supplied by the likes of The Partnership on AI, a collaboration between leading technology companies, academic institutions, civil society groups, and media organisations. The Partnership on AI has, among other assets, compiled a set of principles for responsible AI development. Its multidisciplinary approach makes it ideal for a diverse audience.

Addressing bias in AI is an ongoing, collective effort. For those seeking to delve deeper into this critical issue, Holistic AI’s policy and data science teams have created an extensive repository of white papers, blogs, and webinars which, like the resources above, balance technical depth with ethical considerations. Your engagement in this area, whether through education or other means, helps shape a more equitable future for AI.