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What are thebias and discrimination by AI ? 


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Bias 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 .

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Open accessJournal ArticleDOI
Leying Zou, Warut Khern-am-nuai 
14 Nov 2022-AI and ethics
1 Citations
The paper discusses bias and discrimination in the context of mortgage applications when an AI-based decision support system is used. It shows that there is ethnicity bias in historical mortgage application approvals, and this bias is amplified when an off-the-shelf machine-learning model is used. However, when fair machine-learning algorithms are adopted to alleviate biases, it actually leaves all stakeholders worse off.
Open accessJournal ArticleDOI
Don Dongshee Shin, Emily Y. Shin 
01 Jun 2023-IEEE Computer
The provided paper discusses how bias in algorithms can lead to discrimination, but it does not specifically mention the types of bias and discrimination by AI.
The paper discusses algorithmic discrimination in artificial intelligence algorithms, including original bias, learning bias, and external bias as causes of discrimination.
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 by AI.

Related Questions

What are some examples of bias and discrimination by AI?4 answersBias 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.
What are the bias and discrimination of AI?5 answersBias 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.
What is the Ability of Discrimination in Behavior Analysis?5 answersThe ability of discrimination in behavior analysis refers to the skill of discerning differences among decision situations and choosing appropriate behaviors or actions for each situation. It has been noted that individuals who are unable to make conditional discriminations are less likely to respond differentially in a functional analysis (FA). Discrimination skills are important in various applications, such as noise removal in brain MR images for subsequent processing. In the field of speechreading, the Tadoma method has been used to study the ability of observers to discriminate pairs of speech elements. Additionally, a behavior discrimination system has been developed to accurately discriminate the behavior of a subject based on sensor information. Overall, discrimination abilities play a crucial role in behavior analysis, image processing, and speech perception.
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.
How do we ensure that AI systems are not biased against certain groups of people?4 answersTo ensure that AI systems are not biased against certain groups of people, it is important to address bias at multiple levels. This includes addressing bias in the data used to train AI models, as well as bias in the algorithms and decision-making processes. One approach is to use diverse and representative datasets that include information from vulnerable and marginalized groups. Additionally, there is a need for transparency and accountability in AI systems, with enhanced measures to detect and mitigate bias. Techniques such as data preprocessing, model selection, and post-processing can be employed to mitigate bias in AI systems. It is also important to consider fairness within sensitive groups, in addition to fairness between different groups. This requires the development of AI models that are fair for individuals within the same sensitive group. Overall, addressing bias in AI systems requires a holistic and interdisciplinary approach, involving diverse datasets, ethical considerations, and ongoing collaboration.
How to mitigate and reduce algorithmic discrimination and algorithmic bias?5 answersTo mitigate and reduce algorithmic discrimination and bias, several approaches have been proposed. One approach is to use active instance selection and discrimination penalization to guide limited annotations towards eliminating bias. Another approach is to use a visual interactive tool that incorporates human-in-the-loop AI for auditing and mitigating biases in tabular datasets. This tool uses a graphical causal model to identify unfair causal relationships and fairness metrics, allowing users to refine the model and act on unfair causal edges. Additionally, it is important to address the causes of algorithmic discrimination, such as original bias, learning bias, and external bias, and regulate algorithms with principles of visualization, transparency, and inherent fairness. These approaches aim to ensure fairness, accountability, trust, and interpretability in machine learning algorithms.

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