Why is AI Fairness Important for AI’s future?
Artificial Intelligence presents significant benefits and improvements in the decision-making process. Still, we must be cautious regarding sensitive decisions that can negatively impact individuals and society, such as healthcare, recruitment, education, banking, and justice. For example, AI making decisions based on flawed information reflecting historical inequalities can create a bias and perpetuate unfairness. AI bias is the phenomenon that occurs when an algorithm produces a systemically prejudiced result to erroneous assumptions in the machine learning process.
Multiple ways AI bias seeps into data, such as manipulating, unbalanced data, algorithms and modelling, reflecting biased human decisions or historical human or social inequalities. As a result, AI learns patterns (behaviour) that lack fairness for certain groups of people and put their future and lives at risk.
To make AI fit for modern society, with increased acceptance and fairness toward diversity. We must ensure that AI applications treat everybody equally and in a non-discriminatory manner. Globally, regulators recognise these issues with AI and are introducing mandatory requirements for AI to be fair and unbiased. For AI and organisations adopting AI, it is beneficial to ensure AI fairness – as it will make the organisation socially responsible, meet regulators’ requirements and avoid hefty fines set in AI regulations like EC Artificial Intelligence Act.
How can AI Bias Impact?
The existence of intended or unintended biases in AI can lead to making decisions that can have a collective or disparate impact on specific groups of people. For example, biases in recruitment tools exclude particular groups such as women, biases in facial recognition tools only identify white men and Compass (Correctional Offender Management Profiling for Alternative Sanctions) detects African-Americans were more likely to be assigned a higher risk score and low likelihood of release on bail. The biased decision can deprive people’s ability to work and participate in society. As a result, AI could lose credibility and trust because its decisions are promoting social inequalities. So, AI fairness ensures that AI benefits can be materialised while keeping in check its negative impacts.
What is AI Fairness?
There are numerous definitions of fairness and corresponding mathematical formalisations, such as equal odds, positive predictive parity, and counterfactual fairness. It is a complex and multi-faceted concept that depends on context and culture. Cathy O’ Neil’s award-winning book states that “Weapons of Math Destruction” showed how unfairness harms people’s lives by getting a job, loan, insurance, and fair justice and creating an unjust society. Fairness means the absence of prejudice or preference for an individual or group based on characteristics such as gender, race, age or culture.
Three steps to AI fairness
AI fairness is both a technical, organisation culture and process challenge. There is a three-step process to ensure AI fairness:
Step 01: Set a Definition and Purpose
To achieve this, organisations must set a definition and purpose for AI fairness and non-discriminatory AI behaviour and decisions. To establish this standard definition and purpose of fairness, organisations should involve a diverse group of AI stakeholders – as AI is not just an issue of the data science team.
Step 02: Ensure AI Fairness is part of the DNA
To ensure AI fairness at the core, we need to ensure that data, model design & development, testing & validation and deployment are closely monitored and analysed for discrimination and bias. The organisation has a set standard for fairness; they have deployed real-time monitoring and tracking tool that support all stages of an AI lifecycle.
Step 03: Ensure your AI application is Transparent, Auditable and Accountable
Ensure full AI transparency, auditability and more critical accountability. Setting standards (step 01) and monitoring tools (step 02) is a good start. Still, they are ineffective without a clear accountability framework that tracks and evaluates human and machine actions to be fair continuously and in real-time.
We must combat AI bias collectively and transform our societies by building suitable AI applications are make fair decisions for the betterment of all of us. Ensuring a robust, ethical, and accountable AI ecosystem helps us step closer to making the world more equitable.
How to implement Explainable AI in a Financial Services Application?
AI Fairness in AI solutions is a significant issue and risk for your organisation. All AI regulations and frameworks require fairness and non-discrimination. So, to ensure your AI project provides the desired benefits to your organisation, ensure your AI is fair.
Seclea provides tools for your AI stakeholders (Data Scientists, Ethics Leads, Risk & Compliance Managers) to ensure fair and transparent AI solutions that meet relevant AI regulations, industry standards and your organisations ethical AI policies. We are here to help; email us at email@example.com or fill out the short form.