Can artificial intelligence be biased? And how does that happen?
Introduction:
In the rapidly advancing digital age, artificial intelligence (AI) has become an integral part of our daily lives; it guides content recommendations on social media platforms, enhances healthcare services, manages the financial market, and even participates in judicial decision-making at times. With this widespread presence, a fundamental question arises: Can artificial intelligence be biased? Answering this question leads us to a deeper understanding of how this bias occurs, its effects, and how we can address it.
2. Design Bias: Resulting from the decisions of AI engineers in selecting algorithms and criteria.
3. User Bias: This occurs when users provide biased feedback or reviews.
The lack of fairness testing before publication deepens bias without detecting it.
Using synthetic data generation techniques to cover underrepresented categories.
Equalized Odds: Ensuring that the rate of true positives and false positives is equal across groups.
In-processing: Adding restrictions directed towards justice during training.
Post-processing: Adjusting the model's outputs to reduce bias.
1. What is bias in the context of artificial intelligence?
Bias in artificial intelligence is the tendency of the system or model towards unfair or preferred outcomes for a certain group at the expense of others. This bias can be intentional or unintentional, and it often arises from the data on which the model was trained or the way it was designed.Types of bias
1. Data Bias: When the samples are not representative of all categories.2. Design Bias: Resulting from the decisions of AI engineers in selecting algorithms and criteria.
3. User Bias: This occurs when users provide biased feedback or reviews.
2. The Roots of Bias: Where Does It Begin?
2.1 Data Bias:
Representation Imbalance: When training a model to recognize human faces using images mostly taken from a specific geographic area or age group, it will produce a low-accuracy model when encountering faces outside this category.
Historical Data: In the case of analyzing bank loans, if past data shows a preference for granting loans to males over females, the model will acquire the same bias.
2.2 Design Bias:
Choosing the algorithm: Some algorithms may automatically favor certain characteristics.
Evaluation Bias Issues: Using evaluation metrics that do not take into account the distribution of diverse categories leads us to misjudge the model's performance.
2.3 User Bias:
Feedback Loop: Recommendation algorithms that promote content with high user interaction may reinforce extremist ideologies or a single viewpoint over others.3. Real-life examples of AI bias:
1. Recruitment applications:
Systems (like the Amazon system that was discontinued in 2018) that were evaluating resumes and favoring male candidates over females because they were trained on historical resumes that came from a larger number of male applicants for tech jobs.2. Facial recognition:
Studies (MIT and others) have shown that facial recognition models are less accurate in distinguishing faces with dark skin compared to those with light skin.3. Medical services:
Reports revealed that some algorithms for allocating medical resources in the United States were prioritizing white patients more, as the evaluation system considered the patient's past spending on healthcare as an indicator of their actual need.4. The effects of bias on society:
Inequality:
Bias reinforces existing gaps; under a model controlled by biased data, certain groups may be deprived of job opportunities or access to loans.Loss of trust:
When individuals become aware of instances of AI bias, everyone becomes apprehensive about these technologies, and their adoption may decline.Psychological effects:
The feeling of discrimination by a system that appears "neutral" can cause psychological distress to marginalized individuals and communities.5. How does bias occur technically?
5.1 During data collection:
Relying on certain platforms or online communities to collect data creates a gap in representation.5.2 During data preprocessing:
Removing outliers or missing data without careful examination may eliminate important samples that represent specific categories.5.3 During the selection of the learning model:
Using simplified algorithms or strict criteria for tuning hyperparameters may yield preferred results for average values or more common categories.5.4 During evaluation and publication:
Applying an overall accuracy metric hides the model's poor performance on certain categories.The lack of fairness testing before publication deepens bias without detecting it.
6. Strategies for detecting and addressing bias
6.1 Diversification and comprehensive data collection:
The focus is on collecting samples from diverse backgrounds (gender, race, age, language).Using synthetic data generation techniques to cover underrepresented categories.
6.2 Fairness Metrics:
Demographic Parity: Ensuring that the probability of obtaining a certain outcome is equal across different groups.Equalized Odds: Ensuring that the rate of true positives and false positives is equal across groups.
6.3 Algorithmic Mitigation:
Pre-processing: Data adjustment before training (such as redistribution or calibration).In-processing: Adding restrictions directed towards justice during training.
Post-processing: Adjusting the model's outputs to reduce bias.
6.4 Continuous Human Review:
Integrating multidisciplinary teams (law, ethics, social sciences) at all stages of design and evaluation.7. The role of governments and institutions in regulating fair artificial intelligence:
1. Enacting legislation: such as the European General Data Protection Regulation (GDPR), which grants individuals the right to understand and contest automated decisions.2. Quality standards and labels: such as the "AI Ethics Label" program that evaluates AI systems according to ethical standards.
3. Research funding: Supporting specialized scientific research in understanding and mitigating AI biases.
4. Community awareness: Spreading knowledge about how these systems work and the mechanisms for challenging them.
8. The Future: Towards Fairer Artificial Intelligence
Interactive learning with the user (human-in-the-loop): Involving humans in the decision-making process when necessary to adjust the model's outputs.Explainable AI: Developing models whose decisions can be easily interpreted, revealing examples of bias and allowing for their correction.
International cooperation: Global initiatives to share best practices and secure data and to agree on common ethical standards.