Skip to main content
UploadLanguage (EN)Support
BusinessMobileSocial MediaMarketingTechnologyArt & PhotosCareerDesignEducationPresentations & Public SpeakingGovernment & NonprofitHealthcareInternetLawLeadership & ManagementAutomotiveEngineeringSoftwareRecruiting & HRRetailSalesServicesScienceSmall Business & EntrepreneurshipFoodEnvironmentEconomy & FinanceData & AnalyticsInvestor RelationsSportsSpiritualNews & PoliticsTravelSelf ImprovementReal EstateEntertainment & HumorHealth & MedicineDevices & HardwareLifestyle
Dr.A.Prabaharan Professor & Research Director, Public Action, profile picture
Uploaded byDr.A.Prabaharan Professor & Research Director, Public Action
1,727 views

Artificial Intelligence (AI) and Bias.pptx

The document discusses the intersection of artificial intelligence (AI) and bias, highlighting concerns about fairness, equity, and discrimination in AI systems due to data, algorithmic, selection, feedback loop, and contextual biases. It emphasizes the importance of addressing these biases through fairness metrics, mitigation strategies, and ethical considerations to ensure that AI systems serve all demographic groups equitably. The document concludes that interdisciplinary collaboration and ongoing efforts are necessary to promote social justice in AI-driven decision-making.

Technology◦
Related topics:
Dr A Prabaharan• Public Action• Rukmani Arunachalam• artificial-intelligence-ai•
Downloaded 33 times
Artificial Intelligence (AI) and Bias Dr. A. Prabaharan Professor & Research Director, Public Action www.indopraba.blogspot.com
AI & Bias AI and bias intersect in several ways, raising concerns about fairness, equity, and discrimination in AI systems. Here are some key aspects of AI bias www.indopraba.blogspot.com
Data Bias AI systems learn from historical data, which may reflect existing societal biases and prejudices. If the training data is biased, the AI model may perpetuate or even amplify these biases, leading to unfair or discriminatory outcomes. www.indopraba.blogspot.com
Algorithmic Bias  AI algorithms can encode biases if they are not properly designed, tested, and validated.  Biases may arise from the algorithm's structure, parameters, or optimization objectives, resulting in biased predictions or decisions. www.indopraba.blogspot.com
Selection Bias  Data used to train AI models may not be representative of the population or context in which the AI system will be deployed, leading to selection bias.  This can result in AI systems that are less accurate or effective for certain demographic groups or underrepresented communities www.indopraba.blogspot.com
Feedback Loop Bias  Biased AI systems can perpetuate feedback loops that reinforce existing biases.  For example, biased recommendations made by AI systems may influence user behavior, leading to further biased data collection and model predictions. www.indopraba.blogspot.com
Contextual Bias AI systems may exhibit biases in specific contexts or domains, such as healthcare, criminal justice, or hiring.  Biased AI decisions in these contexts can have significant real-world consequences, impacting individuals' lives and perpetuating systemic inequalities. www.indopraba.blogspot.com
Fairness and Equity  Addressing bias in AI requires considerations of fairness and equity, ensuring that AI systems treat individuals fairly and equitably across different demographic groups and contexts.  Fairness metrics, such as demographic parity, equal opportunity, and disparate impact, can be used to assess and mitigate bias in AI systems. www.indopraba.blogspot.com
Mitigation Strategies  Various strategies can be employed to mitigate bias in AI, including data preprocessing techniques (e.g., data augmentation, debiasing), algorithmic fairness interventions (e.g., fairness- aware algorithms, post-processing techniques), and diversity and inclusion efforts (e.g., diverse data collection, stakeholder engagement). www.indopraba.blogspot.com
Ethical Considerations  Addressing bias in AI raises ethical considerations related to fairness, transparency, accountability, and social responsibility.  Ethical frameworks, such as the principle of beneficence, non-maleficence, autonomy, and justice, can guide the development and deployment of AI systems in a manner that aligns with ethical principles and societal values. www.indopraba.blogspot.com
End Note  Overall, addressing bias in AI is crucial for promoting fairness, equity, and social justice in AI- driven decision-making processes.  It requires interdisciplinary collaboration, stakeholder engagement, and ongoing efforts to identify, mitigate, and prevent biases in AI systems, ensuring that AI technologies serve the common good and contribute to positive societal outcomes.
www.indopraba.blogspot.com