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What are the bias and discrimination of AI? 

Best insight from top research papers

Bias and discrimination in AI systems are significant concerns. Efforts to regulate bias and discrimination in AI systems focus on identifying and minimizing risks rather than outright prohibition . Conversational AI (CAI) in psychotherapy raises ethical risks, as CAI is an algorithm-based system that cannot have a real conversation or relationship . Algorithmic discrimination in AI algorithms manifests as feature-selective discrimination, associative discrimination, and big data-enabled price discrimination . Algorithmic bias in AI systems can generate unfair results and inequalities, potentially leading to discrimination . AI displays biases in the form of data input bias, algorithmic bias, and cognitive bias, influenced by ethnic, gender, intersectional, health, and social biases . These biases can perpetuate or amplify existing biases, highlighting the need for regulation and further research to understand and address AI biases .

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Papers (5)Insight
Book ChapterDOI
13 Oct 2022
1 Citations
The paper discusses three types of biases in AI: data input bias, algorithmic bias, and cognitive bias. It also mentions the incorporation of biases related to ethnicity, gender, health, and social factors.
Open accessJournal ArticleDOI
Don Dongshee Shin, Emily Y. Shin 
01 Jun 2023-IEEE Computer
The paper discusses how bias in algorithms can lead to discrimination and unfair results, but it does not specifically mention the types of bias or discrimination associated with AI.
The paper discusses algorithmic discrimination in AI, including feature-selective discrimination, associative discrimination, and big data-enabled price discrimination. It also mentions the causes of algorithmic discrimination, such as original bias, learning bias, and external bias.
The paper discusses biases in AI applications, including discriminatory biases against certain groups. It highlights how algorithms can perpetuate and amplify existing biases, leading to unfair judgments.
The paper discusses efforts to identify and minimize risks of bias and discrimination in AI systems, particularly in the financial sector. It does not provide specific examples of bias and discrimination in AI.

Related Questions

How can ai be biased?5 answersAI can be biased due to several factors. One of the main causes is the lack of diversity in AI development teams, which can lead to unintentional bias in the models. Additionally, biases present in the real world can be incorporated into AI algorithms if not checked, resulting in algorithmic bias. The data used to train AI models can also introduce bias if it is not representative of the diverse societal groups that may be affected. Biases can arise at different stages of AI development, including the data-generating process, model training, testing, and validation, as well as the interpretation and application of AI models. To mitigate bias in AI, it is important to prioritize diversity in AI development teams, ensure transparent and ethical processes, and conduct thorough testing for bias before deployment. Efforts should also be made to increase awareness and understanding of biases in AI among developers and end users.
What are thebias and discrimination by AI ?4 answersBias and discrimination in AI refer to the unfair treatment or unequal outcomes that can occur as a result of algorithmic decision-making. Algorithmic discrimination can manifest in various ways, including feature-selective discrimination, associative discrimination, and big data-enabled price discrimination. The causes of algorithmic discrimination can be attributed to original bias, learning bias, and external bias. Efforts to regulate bias and discrimination in AI systems focus on identifying and minimizing risks rather than imposing blanket bans. Algorithmic bias can lead to unfair results and inequalities, shaping people's thinking and potentially resulting in discrimination. In the context of mortgage applications, bias has been observed, with black applicants being more likely to be declined a mortgage compared to white applicants with similar circumstances. The use of machine-learning models without human involvement can amplify bias and discrimination, cautioning against their use in decision-making with significant implications.
How does bias impact artificial intelligence?5 answersBias impacts artificial intelligence (AI) in several ways. Firstly, biases can be introduced during the development of AI models, both unintentionally and intentionally, due to factors such as lack of diversity in development teams and the desire for quick results. These biases can lead to unfair outcomes and perpetuate existing inequalities, particularly in areas like healthcareand racial disparities in AI model development. Secondly, biases can arise from the data used to train AI models, as well as the algorithms and human decision-making processes involved. These biases can result in inaccurate healthcare outcomes, skewed results, and errors. To address bias in AI, it is important to prioritize ethical considerations, establish partnerships with stakeholders, ensure transparency and accountability, and explore alternative AI paradigms that prioritize fairness and ethical considerations.
What is main approach to identifying and addressing for AI bias?5 answersThe main approach to identifying and addressing AI bias involves conducting risk assessments and implementing mitigation strategies. Risk assessments are necessary to detect and mitigate bias in AI systems. Statistical approaches, such as the N-Sigma method, can be used to measure biases in machine learning models. Mitigation strategies include data pre-processing, model selection, and post-processing. It is important to address bias at various stages, including data collection, algorithm design, and human decision-making. To ensure effectiveness, interdisciplinary collaboration is needed, along with enhanced transparency and accountability in AI systems. Additionally, efforts should be made to develop diverse and representative datasets and explore alternative AI paradigms that prioritize fairness and ethical considerations. By implementing these approaches, researchers can work towards developing fair and unbiased AI systems.
Wy is AI discriminatory?3 answersAI can be discriminatory due to various factors. Lack of regulation and over-reliance on AI systems can lead to biased outcomes and discrimination against vulnerable groups. Algorithmic decision-making and other types of AI can embed and perpetuate bias and discrimination, posing a significant challenge. Decision-making algorithms may treat users unfairly or unethically based on personal data such as income, gender, ethnicity, and religion, resulting in digital discrimination. The use of AI for profiling can also lead to discriminatory outcomes, despite the belief that intelligent algorithms are free of human prejudices and stereotypes. The need for transparency in AI decision-making systems is crucial to address and prevent discrimination. Overall, the legality of discriminatory AI raises questions about the existing laws and the need for methodological standards to ensure fair and non-discriminatory outcomes.
Why is AI discriminatory?3 answersAI is discriminatory because it can replicate existing biases and incorporate them into algorithms, leading to algorithmic bias. This bias can result in discriminatory outcomes, such as differential treatment and detrimental impact on marginalized groups. AI systems rely on data generated and labeled by humans, which can contain biases from the real world. Discriminatory AI can compromise patient safety, perpetuate disparities in care, and lead to unfair and illegal discrimination based on protected characteristics. Lack of regulation and over-reliance on AI contribute to the occurrence of discrimination. Errors, biases, and opaqueness are inherent in AI systems, which can systemize discrimination and perpetuate social injustices. To address this issue, there is a need for developing methodological standards, raising awareness through AI impact assessments, and implementing ethics by design. Policymakers should regulate AI to prevent discrimination and protect human rights.

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