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Monday, 18 September 2023

What Is Bias In AI? Understanding And Addressing Ethical Challenges

Artificial intelligence (AI) and machine learning (ML) are transforming our world at an astonishing pace, with these systems offering unheard-of capabilities for automation, decision-making, and data analysis.

While AI and ML are the gifts that keep on giving, questions are bound to arise concerning the latitude that AI is given and what constitutes a breach of ethics.

Bias In AI

One of the most critical ethical issues in AI is bias. Bias in AI occurs when disparities exist between data sets, often due to preconceived notions in algorithm development or inherent prejudices within training data.

In this quick read, we’ll seek to understand the nuances of AI bias, explore its recent examples, and discuss how it creeps into AI systems.

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Understanding Bias In AI

Generally speaking, bias in AI/ML systems occurs when prejudices are propagated by erroneous assumptions in these systems.

Usually, these biases may not even be detected until the software is up and running. Such biases are usually multi-pronged, and they may manifest severally. 

Recent examples illustrate the urgency of addressing AI bias:

1. AI Recruiting Tool Bias

A prominent technology conglomerate had to abandon an AI-based recruiting tool as it exhibited bias against women. This instance underscores how AI bias can perpetuate gender disparities.

2. Racist AI On Twitter

A leading software enterprise issued an apology when its AI-based Twitter account started posting racist comments. AI's potential to spread hate and discrimination is a grave concern.

3. Biased Facial Recognition

A well-known technology company faced criticism and halted its facial recognition tool due to bias against certain ethnicities. This example highlights the real-world consequences of biased AI.

4. Racial Bias In Image Cropping

A major social media platform faced backlash for its image-cropping algorithm, which exhibited racial bias by prioritizing White faces over faces of color. This incident exemplifies how AI can perpetuate societal inequalities.

5. Contrastive Language-Image Pretraining (CLIP) Bias

The Artificial Intelligence Index Report 2022 revealed that in contrastive language-image pretraining experiments, images of Black people were misclassified as nonhuman at more than twice the rate of any other race.

The report also noted that AI systems misunderstood Black speakers, particularly Black men, twice as often as White speakers in previous experiments.

How Does Bias Creep Into AI Systems?

Bias creep in AI is an important theoretical and practical topic, and is complex enough to make students pay for someone to write essay. Start your journey right here. 

To understand how bias enters AI systems, let's consider one type of bias, which is coverage bias illustrated by the figure above.


An AI program or algorithm is developed and trained using test data, which shapes its logic based on various data scenarios.

For the model above, the bias can be propagated by applying profiles erroneously due to a limited population of training data, compared to the more general population on which the model will be used. 

Once tested, the AI program processes live data, providing results that are analyzed to further refine its logic. This feedback loop continues, allowing the machine to learn and evolve.

Bias and ethical concerns in machine learning and AI development can be attributed to external and internal factors within organizations.

External Factors

Biased Real-World Data: AI algorithms trained on real-world data inherit the biases present in that data. Overrepresentation of certain groups can skew AI results.

Lack of Guidance: Although countries and organizations have begun regulating AI, frameworks often provide high-level principles without actionable procedures. Tailoring these frameworks to specific AI systems can be challenging.

Biased Third-Party AI Systems: Outsourcing AI development to third parties can result in insufficient validation for bias, as organizations rely on external expertise.

Internal Factors

Lack of Bias Focus: Data scientists and engineers may prioritize technical performance over bias identification, especially in fast-paced tech environments.

Nondiverse Teams: Teams lacking diversity may struggle to identify bias effectively, especially in contexts involving underrepresented groups.

Nonidentification of Sensitive Attributes: Failure to identify sensitive attributes, such as gender or race, can lead to unaddressed correlations that perpetuate bias.

Unclear Policies: Traditional organizational policies often do not cover AI-specific concerns, such as bias identification and removal.

Mitigating Bias In AI

Addressing AI bias requires a multifaceted approach involving entity-level and process-level controls:

Entity-Level Controls

Establish AI Governance and Policies: Organizations must adapt their policies and control frameworks to incorporate AI systems. Internal controls, data collection protocols, and periodic AI output reviews are essential for bias-free AI development and operation.

This step involves creating an organizational culture that prioritizes ethical AI practices.

Diverse and Inclusive Teams: Building diverse teams with varied perspectives can help identify and mitigate bias effectively.

Teams that include individuals from different backgrounds and experiences are more likely to recognize and rectify bias in AI systems.

Process-Level Controls

Data Preprocessing: Rigorous preprocessing of training data is crucial. This includes identifying and addressing biased data sources, carefully curating data sets, and applying techniques to mitigate bias during the data preparation phase.

Fairness Metrics: Implement fairness metrics during AI model training and evaluation. These metrics assess whether AI systems exhibit bias towards specific demographic groups, helping to identify and rectify disparities.

Explainable AI (XAI): Develop AI models with transparency in mind. XAI techniques provide insights into how AI systems make decisions, enabling stakeholders to understand and address potential bias.

Continuous Monitoring: Implement continuous monitoring of AI systems in real-world applications. Regularly assess the system's performance, detect bias, and fine-tune models to reduce disparities as they arise.

Bias Impact Assessment: Conduct thorough impact assessments to understand how AI decisions affect different groups. This process can help organizations anticipate and address unintended consequences.

Ethical Audits: Periodically conduct ethical audits of AI systems to ensure they align with established ethical guidelines and regulatory requirements. These audits can identify and rectify bias at various stages of AI deployment.

In Conclusion

Addressing bias in AI is not a one-time task or event, but rather requires an ongoing commitment.

It requires a proactive approach involving diverse teams, rigorous controls, and collaboration with stakeholders.

By prioritizing ethical AI development, organizations can harness the power of AI while minimizing the harm caused by bias.

As the AI landscape continues to evolve and ethics becomes a part of the system, all stakeholders alike should ring in mechanisms to avoid or contain such biases before they have negative ramifications. 

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