Artificial intelligence (AI) systems are being deployed in high stakes domains like healthcare, criminal justice, and employment. However, there is growing evidence that these AI systems are amplifying biases against marginalized communities. Left unchecked, this threatens to exacerbate discrimination and deny opportunities to large segments of the population.
Concern around societal impacts of biased AI systems seeing rapid growth
In this comprehensive guide, we cover different facets of understanding, identifying and mitigating unwanted biases in AI systems.
What is AI Bias?
AI bias refers to situations where an AI system produces systematically prejudiced results due to:
- Biases in the training data
- Biases in the algorithms/modeling methodology
These biases manifest as unfair discrimination against certain population subgroups and individuals. For example, an AI system for screening job candidates might disproportionately reject female applicants due to historical biases in the hiring data.
It‘s important to note that while human decisions can also be biased, AI systems have the capability to negatively impact millions of users at scale rapidly. Proactively identifying and mitigating biases is therefore crucial.
Why is AI Bias a Big Concern?
- Scale – Small biases negatively impact millions of users once deployed at scale
- Harm – Discrimination exacerbated especially for marginalized communities
- Trust Deficit – Erodes public trust and acceptance of AI technology
For example, studies have revealed that facial analysis systems from vendors like Microsoft and IBM perform much worse on non-white faces compared to white faces. Such tools can then enable mass surveillance systems to disproportionately target minorities.
Common Sources of Bias
Biases creep into AI systems in subtle ways:
1. Biases in Data
Since AI systems learn patterns from data, any historical biases present in the dataset gets propagated. Common data issues include:
- Sampling bias: The data is not representative of the target population. For e.g., AI models trained only on high resource clinical populations.
- Measurement bias: Systematically inaccurate data due to flawed measurement processes. For e.g., subjective human annotator biases in content classification tasks.
- Label bias: Outdated/offensive class labels that reinforce stereotypes.
- Reporting bias: Systematic underreporting of issues due to mistrust of authorities.
Due to such data quality issues, certain population groups might be underrepresented as the chart above shows. Hence models inadvertently discriminate against them.
2. Biases in Algorithms
Apart from data biases, the algorithms and modeling methodologies themselves can introduce biases:
- Aggregation bias: High dimensional interactions between variables are oversimplified. Correlations might work reasonably well overall but fail for underrepresented groups.
- Evaluation bias: Model performance metrics might not sufficiently capture negative impacts on disadvantaged groups.
- Overreliance on correlations: Spurious correlations result in models associating meaningless patterns with outcomes.
For example, an algorithm predicting risk of re-offense can be biased against certain ethnicities if it relies solely on correlational patterns in the data without accounting for confounding factors.
AI systems have blindspots that perpetuate historical biases and discrimination
This highlights how bias in AI is an ethical issue with major social implications.
Next, let‘s examine a few real-world case studies highlighting harmful biases to build intuition before diving into technical solutions.
Examples of Biased AI Systems
1. Racial Bias in Healthcare Algorithms
A widely used healthcare algorithm for prioritizing patients was found to systematically assign more resources to white patients than sicker black patients.
| Patient Group | Resources Assigned | Actual Medical Needs |
|---|---|---|
| White | Higher | Lower |
| Black | Lower | Higher |
Cause: The algorithm relied solely on healthcare costs as a proxy for medical needs. However, this fails to account for disparities in access to healthcare among groups. The biases resulted in discrimination against ethnic minorities.
2. Gender Bias in Amazon‘s Recruiting Tool
An AI-powered resume screening tool developed by Amazon was scrapped in 2017 when it was found to penalize resumes containing the word "women’s".
Cause: The system was trained on predominantly male resumes given historical gender imbalance in the tech industry. This resulted in gender discrimination during candidate screening.
3. Racial Bias in Facial Recognition Systems
Studies have revealed that facial analysis systems from vendors like Microsoft and IBM perform much worse on non-white faces compared to white faces.
Cause: Lack of racial diversity in the training datasets leads to poor generalizability. This can enable mass surveillance systems to disproportionately target minorities.
These examples demonstrate how bias perpetuates discrimination and exclusion despite AI systems being deployed for seemingly objective tasks like screening resumes or medical diagnosis.
Let‘s look at solutions now.
How to Mitigate Unwanted Biases in AI Systems?
Debiasing AI systems is technically challenging because biases manifest in subtle ways. However, adopting responsible AI practices can help reduce harms. Best practices include:
Improve Training Data Quality
As the saying goes – garbage in, garbage out. Rigorously auditing data and proactively identifying sampling, labeling and measurement biases is crucial before model development begins. Strategies like oversampling minority groups can also help.
Domain expertise is invaluable here. For example, seeking inputs from social scientists and marginalized communities can reveal blindspots.
Apply Algorithmic Debiasing Techniques
Techniques like adversarial debiasing and causal modeling help reduce reliance on spurious correlations.
For example, IBM‘s AI Fairness 360 toolkit contains algorithms focused on fairness, explainability, and mitigating unwanted biases.
That said, purely algorithmic solutions have limitations. Real-world biases involve complex societal factors whereas models only see correlations.
Monitor Models Closely
Continuous feedback loops enable identifying biases before vast damage is done. For example, routinely testing model performance across user subgroups and maintaining gender/racial distribution metrics can surface group-specific performance drops indicative of biases.
Incorporate Human Oversight
Certain high stakes decisions like loan approval might need human-in-the-loop frameworks to detect anomalies flagged by users. Hiring human auditors (like Facebook’s civil rights team) also enables sustained expertise.
Promote Diversity in Teams
Diversifying teams helps surface blindspots early in the development life cycle. However, the onus should not lie solely on marginalized communities. Allies also need to educate themselves on responsible AI practices.
Steering the AI field towards broader ethical considerations requires sustained engagement from all stakeholders – companies, governments, researchers and civil society groups. Getting there necessitates open and honest conversations onexisting limitations.
Next, let‘s dive deeper into the open challenges.
Ongoing Challenges in Eliminating Bias
Despite increased awareness, completely eliminating unwanted biases in AI remains an open challenge:
Difficulty in Formalizing Real-World Fairness
While theoretical definitions help scope the problem, real-world systems need quantifiable metrics tailored to application domains to enable audits and monitoring. For example, how do we monitor long term impacts of candidate screening algorithms in recruiting? Industry adoption of standards here is still lagging behind academic advances.
Inherent Tradeoffs Between Fairness and Accuracy Goals
Algorithmic changes to improve equity metrics like statistical parity often negatively impact overall predictive accuracy. For example, once a biased dataset is corrected, model performance can degrade significantly. Resolving this tension itself is an active area or research.
Accounting for Evolving Biases
Biases can get introduced during ongoing maintenance via changes in data distributions or human feedback loops. Production systems require systemic accountability through continuous audits to account for such drift.
Role of Human Cognitive Biases
Humans are still required to some extent to identify problems – our cognitive biases pose limitations here. Interdisciplinary perspectives linking social psychology and machine learning are crucial to address the interplay between human and algorithmic biases.
Nevertheless, not striving for fairness also has tangible harms as the examples illustrated earlier highlight. Pragmatic approaches rooted in ethics can help guide technology to uphold human values.
Increasing industry investments into responsible AI reflects growing mainstream urgency
We‘ve only scratched the surface on why algorithmic bias poses an existential threat if left unaddressed. For a deeper dive, refer to the additional resources below on emerging techniques and best practices around building fair and transparent systems.
If working on mitigating unintended consequences of technologies for the underprivileged appeals to you, exploring careers in AI ethics and policy might be worthwhile! Together, we can steer innovation towards just ends.
