Exploring AI Bias "Ethical Implications in Technology"
Ethical Considerations
Significance of AI Bias
Challenges and Limitations
Future Implications
Impact of AI Bias
Ethical Frameworks for AI
Economic Implications
Legal Implications
Social Implications
Biased AI algorithms can reinforce stereotypes, leading to marginalized groups facing increased barriers to opportunities and resources.
Trust in AI systems may erode among affected communities, leading to decreased adoption and utilization of technology.
AI bias can perpetuate and exacerbate existing societal inequalities by favoring certain demographic groups over others in decision-making processes such as lending and hiring.
Unfair treatment in hiring and promotion decisions based on biased AI algorithms can hinder career advancement opportunities for affected individuals
AI bias can lead to economic disparities by disadvantaging individuals and communities in access to employment, education, and financial services.
Failure to address AI bias adequately may result in reputational and financial consequences for organizations responsible for deploying biased AI systems
Legal frameworks may struggle to keep pace with rapid advancements in AI technology, leading to ambiguity and uncertainty in addressing AI bias
Kantianism would state that they should share human values, where they use ethical reasoning which includes being fair.
Utilitarianism would state that AI should function to produce maximum happiness
Rule Utilitarianism, states that Ai should be using ethical practices that prioritizes the well being of society.
AI functions off of past data, so if the data is biased, then the AI will amplify these biases.
Will the AI act in a manner that is fair to everyone it will impact?
Types of AI Bias
Will the AI act in a manner that impacts individuals in a positive way?
Selection Bias: Happens when the data used to train an AI system doesn't represent the way it's supposed to model.
Confirmation Bias: Happens when an AI system is relied on pre-existing beliefs in the data.
Measurement Bias: Occurs when the data collection differs from actual variables of interest.
Will the AI be constructed in a manner which seeks to mitigate biases?
Could limit job opportunities, privacy, and ethical concerns by amplifying bias data. This could establish a butterfly effect.(Many minor changes create a major change)
Stereotyping Bias: Happens when an AI system reinforces harmful stereotypes.
Out-group Homogeneity Bias: When it happens, an AI system becomes less capable of distinguishing between individuals who are not part of a majority group in training data's.
Economic disparities may arise that would affect access to services and opportunities. This ultimately will heighten the wealth gap between demographic groups.
Fairness and Equity: Can perpetuate and reinforce existing societal biases and inequalities.
Non-Discrimination and Equal Opportunities: ensures that decisions and opportunities provided by AI are not influenced by promoting non-discrimination and equal opportunities for all
Avoiding Harm and Unintended Consequences: can cause harm by reinforcing stereotypes, discriminatory practices, or by excluding certain groups from opportunities.
Trust and User Confidence: helps users enhance trust with AI.
lose the trust of the public, through continued AI bias will lead to a bad public image of technology and their manufacture.
Long-Term Viability and Sustainability: contributes to long-term viability and sustainability of AI technologies.
Since AI can have a bias with data or algorithms defining a clear task could cause trouble.
Inclusivity and Diversity: ensure that the AI systems can consider a broad range of perspectives and data points so that it can help accommodate diverse populations.
Constraints for future of AI development. If AI becomes disowned or distrusted by the public, investors will not invest into future AI projects.
Is the AI constructed / acting in a transparent manner?
Stricter regulations on AI development. With more restrictions on new AI projects, this will cost companies vast resources and investments. In the end, this will drastically affect the amount of money a company will make from an AI product.
AI bias has also effected model validation and development. This means the data could be skewed.
Various legal and ethical challenges. With the ever-growing complexity of AI based systems, legal frameworks may have a hard time with staying up to date on all of the technological advances. For this reason, it will lead to questions surrounding liability, accountability, and charges against a person or entity if found liable and guilty.
AI, and the people who create AI, have a duty to ensure that the AI does not interfere with individual's rights.
Healthcare inequality. With AI advancing at a rapid pace, hospitals now use and implement AI within some hospitals. This AI mainly analyzes data to help give a speedy and more accurate result. Although this may help, the AI is still taking into account of the patents information, being available to it instantly. Which all very well could lead to unequal health care for different groups.