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What are some examples of bias and discrimination by AI? 


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Bias and discrimination by AI can manifest in various ways. One example is algorithmic discrimination, which includes feature-selective discrimination, associative discrimination, and big data-enabled price discrimination . Another example is the use of AI in hiring decisions, where discrimination based on gender can occur . Additionally, bias can be present in the detection of skin color, leading to discriminatory outcomes . In the field of medicine, AI models have been shown to exhibit racial bias, such as assigning higher estimated glomerular filtration rate (eGFR) to patients identifying as Black . The Kidney Donor Risk Index is another model that assigns higher predicted risk of kidney graft failure to patients identifying as Black, potentially exacerbating inequality in access to organs for transplantation . These examples highlight the need for regulation and mitigation strategies to address bias and discrimination in AI systems .

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Book ChapterDOI
13 Oct 2022
1 Citations
The paper identifies three types of biases that affect AI: data input bias, algorithmic bias, and cognitive bias. It does not provide specific examples of bias and discrimination by AI.
The paper discusses examples of bias in AI, such as racially biased eGFR estimates and the Kidney Donor Risk Index assigning higher predicted risk to Black patients.
Open accessBook ChapterDOI
02 Nov 2022
2 Citations
The paper provides three examples of bias and discrimination by AI: gender discrimination in hiring decisions, discrimination in law enforcement, and bias in the detection of skin color.
The paper mentions examples of algorithmic discrimination, including feature-selective discrimination, associative discrimination, and big data-enabled price discrimination.

Related Questions

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.
Elements contributing to biases in AI algorithms?5 answersBiases in AI algorithms can be attributed to several factors. One major factor is the lack of gender and social diversity in AI development teams, which can lead to unintentional bias. Another contributing factor is the haste from AI managers to deliver results, which may overlook ethical considerations and the potential impact on society. The testing phase before model deployment is also a risk for bias, as it often fails to represent the diverse societal groups that may be affected. Additionally, biases can arise from the training data fed into the AI system or the design of algorithmic models. Historical biases encoded in the data used for training AI systems can also contribute to bias in algorithmic outcomes. It is crucial for responsible organizations to prioritize fair processes, ethical considerations, transparency, and adequate testing for bias in AI development.
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.
What are some statistics surrounding bias in AI?5 answersBias in AI is a growing concern, with 81% of technology leaders expressing a desire for government regulation in this area. Detecting and mitigating bias in AI can be challenging, as it is more abstract and unintuitive than traditional forms of discrimination. Two metrics, Combined Error Variance (CEV) and Symmetric Distance Error (SDE), have been proposed to quantitatively evaluate class-wise bias in multi-class classifiers. AI-based systems have the potential to impact individuals and society, raising concerns about human rights issues. Studies have shown bias in face recognition systems, crime recidivism prediction tools, and natural language processing tools. The use of automated decision-making tools is more prevalent among lower socioeconomic classes, exacerbating bias. Researchers and scholars have proposed various approaches to mitigate bias in AI, but there is still room for advancement in this area.
What is bias in artificial intelligence?5 answersBias in artificial intelligence (AI) refers to the tendency of AI systems to produce results that are unfair or discriminatory towards certain groups. AI systems rely on data that is generated and labeled by humans, and if this data contains biases, those biases can be incorporated into the AI algorithms. This can result in AI systems replicating and even amplifying existing social biases, leading to negative consequences such as perpetuating disparities in care and outcomes. Bias in AI can arise at different stages, including the data-generating process, model training and testing, and the interpretation and application of AI models. It is important for researchers and developers to be aware of bias in AI and to implement strategies to mitigate and prevent bias, such as algorithmic debiasing methods and nonalgorithmic bias mitigation approaches. By addressing bias in AI, we can work towards developing more fair and equitable AI systems that benefit all individuals and communities.
Has AI ever been used to detect bias in hiring?5 answersAI has been used to detect bias in hiring. Studies have shown that AI-based systems can reduce discrimination against older and female candidates but may cause fewer selections of foreign-race candidates. In the domain of ML-assisted hiring, high-performance models significantly improve human performance in a hybrid setting, but some models mitigate hybrid bias while others accentuate it. Machine learning and AI applications in HR, such as candidate screening, can have human biases carry over to decisions made by a model. Companies offering algorithmic pre-employment assessments have made efforts to detect and mitigate bias, but the choices they make regarding data collection and prediction targets pose risks and trade-offs.

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