Dr. Broderick Turner and Dr. Karim Ginena discuss the importance of responsible and fair AI, challenges in the workplace and marketplace, demystifying AI language, addressing bias and data privacy, and potential solutions for responsible AI use.
Including diverse perspectives in the development of AI systems is crucial for fairness and avoiding bias.
Understanding the building blocks of statistical models empowers individuals to question and evaluate the outputs of AI algorithms.
Deep dives
The Importance of Including Diverse Perspectives in AI Development
One of the main challenges in responsible and fair AI is the lack of representation in the data, classification, and coding processes. Including a wide range of perspectives is crucial to ensuring equitable technology products. This involves incorporating diverse data sources, engaging different stakeholders, and building inclusive teams. By doing so, companies can avoid bias and develop products that better serve the needs of all users.
Demystifying AI: Understanding the Inner Workings of Machine Learning Models
A key insight is that AI systems are not magical black boxes. They are based on statistical models with parameters that represent opinions and biases. By demystifying AI and understanding the building blocks of statistical models, such as the equation y = mx + b, individuals can better comprehend the decision-making processes behind AI systems. This empowers consumers and employees to question and evaluate the outputs of AI algorithms and ensure they are accurate, unbiased, and fair.
Addressing Unfairness, Hallucination, and Data Privacy Challenges in AI
To foster responsible and fair AI, it is crucial to address key challenges. These include tackling unfairness in AI systems that result from biased training data, rectifying the problem of hallucinations where AI models generate misleading or false information, and enhancing data privacy and security measures. Measures such as legislation, transparent disclosure of AI system development, inclusive data collection, human oversight, and user feedback mechanisms are essential to mitigate these challenges successfully.
Recommendations for Companies, Consumers, Researchers, and Students
For companies, it is important to adopt a comprehensive approach to AI that prioritizes responsible AI infrastructure and considers fairness, privacy, and robustness. Consumer awareness is key, and individuals should not blindly rely on AI outputs, but critically evaluate them and trust their own judgment. Researchers play a vital role in conducting audits, advocating for transparency, and championing inclusive data practices. Students are encouraged to develop creative skills and express themselves authentically, as there will continue to be a demand for human creativity.
Wharton’s Stephanie Creary speaks with Dr. Broderick Turner -- a Virginia Tech marketing professor who also runs the school’s Technology, Race, and Prejudice (T.R.A.P.) Lab -- and Dr. Karim Ginena -- a social scientist and founder of RAI Audit -- on how to use AI while thinking critically about its flaws.