Algorithmic Bias: An Overview
Algorithms are sequences of instructions used for solving problems or completing computational tasks. Increasingly, algorithms are being used to make important decisions in various domains such as healthcare, criminal justice, education, and employment. However, algorithms can perpetuate, amplify, or introduce bias if not properly designed and monitored. This has given rise to concerns about "algorithmic bias".
What is Algorithmic Bias?
Algorithmic bias refers to systematic and repeatable errors in a computer system that create unfair outcomes for certain groups of people. This can stem from several factors:
Biased data: If the data used to train an algorithm itself contains biases, this gets reflected in the algorithm’s decisions. For example, if a predictive policing algorithm is trained on records with racial bias, it could designate certain minorities as more prone to crime.
Poor program design: The way algorithms are coded can also introduce bias. Design choices on how data is classified, filtered, or labeled could negatively impact marginalized groups.
Lack of diversity in development teams: Homogenous teams building algorithms run the risk of having limited perspectives. Unconscious biases could seep into development and get baked into the algorithms’ logic.
Effects of Algorithmic Bias
Biased algorithms can scale discrimination and deny opportunities for entire groups of people leading to real-world harm:
Credit algorithms - biased algorithms could deny loans and mortgages to qualified individuals just based on their zip codes, ethnicity, or gender. This exacerbates financial inequalities.
Hiring algorithms - machine learning models trained on biased recruiting data from predominantly white male industries could discount women and minority resumes when screening candidates. This creates unfair barriers to economic mobility.
Healthcare algorithms - several studies have shown medical diagnosis and risk assessment algorithms exhibiting racial bias against black patients by underestimating their needs. These judgment errors can be life-threatening.
Mitigating Algorithmic Bias
Thankfully, once stakeholders become aware of algorithmic bias, good practices can help tackle the problem:
Carefully screening data for biases and removing proxies that enable discrimination on protected attributes
Having human-in-the-loop checks before algorithms make high-risk determinations
Making algorithms transparent about their decision policies so humans can notice unfairness
Using techniques like adversarial debiasing to reduce learned biases
Actively seeking to build diverse data science teams
Responsible algorithm design is crucial for an equitable future with AI. Understanding algorithmic bias and its consequences is the first step organizations must take towards that vision. With vigilance and foresight, we can develop algorithms that uplift instead of further marginalizing vulnerable communities.
Editor, CEO, & Founder: Aepeli.com
