LIVE Chat (Recorded): AI Ethics and Bias

LinkedIn LIVE Audio event recorded on Tuesday, June 18th 2024.

As we embrace the power of artificial intelligence, let's not forget the responsibility that comes with it. Addressing AI ethics and bias is crucial for a fair, accountable, and inclusive future.

🎧Fairness: AI should treat everyone equally, regardless of background or identity.
🎧Transparency: Let's demystify output. understand how AI decisions are made.
🎧Mitigating Bias: Detect and correct biases in training data and algorithms.
🎧Human-Centric Design: Prioritize human well-being over blind automation.

Together, we can build AI that uplifts humanity! 

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Transcript

WELCOME to The Digital Revolution with Jim Kunkle, I appreciate you joining this LIVE chat session here on LinkedIn Audio.

Over the last few months of The Digital Revolution with Jim Kunkle podcast I have been highly satisfied with the reception of the audio podcast content and topics.  If you’re not following or a subscriber of the podcast, please after this LIVE chat session ends, search “The Digital Revolution with Jim Kunkle” on your favorite podcast provider and set your follow or subscribe to the podcast.  You’ll find the podcast on providers like Apple Podcasts, Spotify, Amazon Music, and I-Heart Radio.

“Before I set-up today’s topic, I’d like to cover how you can participate in this LIVE session…It's easy, all you have to do is use the “RAISE” your hand icon in the chat menu.  Also please feel free to hit any emoji to let me know how you're feeling about this LIVE chat?”

I am recording the session audio to re-broadcast this chat on the Digital Revolution channel on YouTube and bonus audio release on the podcast.

Our topic is: “AI Ethics and Bias”.

OK…As artificial intelligence continues to shape our world, it's essential to address the ethical impact and potential biases inherent in AI systems. From algorithmic decision-making to data collection, I'll explore what AI experts point out as challenges, best practices, and ongoing efforts to create fair, transparent, and accountable AI.

Let’s get into the topic…

So what does it mean “ethics in artificial intelligence”? 
Artificial Intelligence ethics refers to the set of guiding principles that stakeholders, from engineers to government officials, use to ensure that artificial intelligence technology is developed and used responsibly. It involves taking a safe, secure, humane, and environmentally friendly approach to AI. In essence, AI ethics seeks to optimize AI's beneficial impact while minimizing risks and adverse outcomes. As AI continues to shape our world, addressing ethical concerns becomes crucial to create fair, transparent, and accountable systems.

Now that I laid a base foundation on AI ethics, let me do the same related to bias in relation to AI.

Bias in artificial intelligence refers to the occurrence of biased results due to human biases that skew the original training data or AI algorithms. Machine learning is how AI learns, training data typically comes from a wide collection of data from the Internet.  When bias exists, it can lead to distorted outputs and potentially harmful outcomes. These biases can manifest in various ways, such as racial, gender, or age bias, and they impact how AI systems make decisions. While computational and statistical sources of bias are crucial to address, it's equally important to consider the broader societal context in which AI systems operate. Context matters, and understanding bias requires a socio-technical approach that goes beyond algorithms and data to encompass societal influences.

So what are the consequences of bias in relation to AI? 

Bias in artificial intelligence has far-reaching consequences, impacting both individuals, businesses and organizations. Let's talk about some of these consequences:

First is Reduced Accuracy: Bias reduces AI's accuracy, compromising its potential. When AI systems produce distorted results due to bias, businesses are less likely to benefit from their deployment of Artificial Intelligence resources and systems.

Second, Harm to Humans: AI decisions affect people's lives. Biased AI can unintentionally use discriminatory practices, harming marginalized groups. For instance, biased hiring algorithms may reinforce existing in-equalities (IN-ECK-Ualities).

Third, Reputation Damage: Scandals resulting from AI bias can erode trust. Businesses and organizations risk reputational damage if their AI systems exhibit or delivery bias decisions.

Last and impactful is mistrust: Bias fosters mistrust among people of color, women, people with disabilities, and other marginalized groups. Trustworthy AI requires addressing bias comprehensively.

SO, mitigating AI bias is essential for fairness, accuracy, and societal trust in AI resources and systems.

How can businesses and organizations looking to incorporate AI into their operations address and mitigate ethical issues from AI?

When incorporating AI into their operations, businesses and organizations must proactively address and mitigate ethical issues. Here are recommended practical steps they can be taken:

First, Identify Existing Infrastructure: Leverage existing industry-available infrastructure that supports data and AI ethics programs. This ensures alignment with business and organizational processes and resources.

Second, Create an Ethical Risk Framework: Tailor a data and AI ethical risk framework to your specific industry. Always focus on factors like fairness, transparency, corrective actions and importantly accountability.

This is an important real-world and proven recommendation! Learn from Health Care: Take cues from successful ethical practices in health care. Prioritize customer and prospects well-being, informed consent, and data + identity privacy. Apply these principles to AI systems.

Establish Guidance for Managers: Optimize guidance and tools for managers. Equip them with ethical considerations during AI integration and implementation.

Always Build Organizational Awareness: Foster awareness across the organization. Educate employees about AI ethics, emphasizing responsible use and potential liabilities and risks.

Consider Incentivizing Employee Participation: Formally and informally incentivize employees to identify external and/or internal AI ethical liabilities and risks. Encourage a culture of speaking-out and professional responsibility.

Last and extremely important is to Monitor Impacts and Engage Stakeholders: Continuously monitor AI impacts. Engage stakeholders to ensure alignment with ethical goals.

Take this away, AI ethics isn't just a theoretical concern, it's a critical business necessity. By following the industry experts recommended steps that I just mentioned, companies can navigate the complexities of AI while maintaining trust and accountability.

How can businesses and organizations address bias in AI training data?

Addressing bias in AI training data is crucial for creating fair and reliable AI resources and systems. Here are AI industry recommended practical steps businesses and organizations can take:

Establish Diverse and Representative Data: Ensure that the training data is diverse and representative of the population. Collect, use, and publish data lawfully while considering intellectual property rights and privacy.

Regular Evaluation: Evaluate AI outputs regularly to minimize biases and inaccuracies. Ongoing monitoring helps identify and rectify bias throughout the development process.

Raise Awareness: Educate stakeholders about AI ethics and the importance of addressing bias. Awareness fosters a culture of responsible AI use.

Remember, transparent and unbiased training data is the foundation for ethical AI.

Many companies creating AI via machine learning do not have a mass of data of their own to properly train artificial intelligence, so the majority of machine learning is processed with 3rd party data.  So how can businesses and organizations handle bias when using 3rd party data?

When businesses and organizations incorporate third-party data into their operations, addressing bias becomes crucial. Here are some practical steps to handle bias when using external data sources:

First is Robust Data Governance: Implement robust data governance practices, including data validation, cleansing, and enrichment. Poor data quality can lead to erroneous risk assessments, while biased data perpetuates unfair treatment of suppliers or third parties.
Second have Diverse and Representative Datasets: Invest in diverse and representative datasets. Ensure that the third-party data reflects a wide range of perspectives and demographics.

Third, establish Fairness-Aware Machine Learning: Use "fairness-aware" machine learning techniques. These methods help mitigate bias during model training and decision-making.

Next is Transparency and Accountability: Ensure transparency in your decision-making processes. Document how third-party data is used and how biases are addressed.

As always it’s important to have Continuous Monitoring and Auditing: Regularly monitor and audit third-party data to verify its accuracy and completeness. Bias can emerge over time, so ongoing vigilance is essential.

By following these steps, businesses can harness the value of external data while minimizing bias and ensuring ethical practices. 

I think it’s important for me to also cover some real-world examples of companies successfully managing third-party data biases?

Real-world examples always shed light on how companies address third-party data biases in AI. Here are a few notable examples:

Amazon's Gender-Biased HR Model: Amazon faced criticism when its AI-driven hiring tool exhibited gender bias. The model favored male candidates over female applicants, reflecting historical biases in the training data.

Google's Racially-Biased Hate Speech Detector: Google's hate speech detection system showed racial bias, misclassifying certain content. This highlighted the need for rigorous bias detection and mitigation.

(LA-Tanya) Latanya Sweeney's Research: Harvard researcher (LA-Tanya) Latanya Sweeney found that online search requests returned different results based on names associated with race. Let me expand on what I just said, here’s examples of AI bias based on names that are commonly associated with race.

Language Models and Pricing: A study found that large language models or LLMs as they are known treat queries differently based on first and last names suggestive of race or gender. For instance, asking an LLM about the price of a used bicycle being sold by someone named "Jamal" yields statically higher a different (lower) dollar amount compared to the same request using a seller's name like "Logan" which is widely seen as belonging to a caucasian male.

Facial Recognition Algorithms: Training data for facial recognition algorithms that over-represent caucasian people can lead to errors when recognizing people of color. Biased data can perpetuate racial disparities in AI applications and intelligent tools.

Hiring Study with GPT: OpenAI's GPT-3.5 exhibited bias against African American names in a hiring study. Resumes with distinct Black American names were less likely to be ranked as top candidates for certain roles.

Next I’d like to talk about the COMPAS Algorithm: The COMPAS algorithm, which is used in US court systems to predict reoffending probabilities. COMPAS faced scrutiny for bias as it demonstrated and delivered racial disparities in its predictions.

These few examples underscore the importance of vigilant monitoring, transparency, and ongoing efforts to combat bias in AI systems. 

Now takeaway from what I just covered during this LIVE chat, regarding ethics and bias in AI:

Bias in AI: Algorithms can inadvertently perpetuate discrimination or inaccurate decision-making due to biased assumptions during development or prejudices in training data. Understanding the factors contributing to bias is essential for effective mitigation.

Complexity of AI: AI isn't inherently good or bad; it's a complex tool. Acknowledging this complexity helps us navigate its impact more effectively.

Maximizing Benefits: World leaders are actively exploring how to maximize AI's benefits while minimizing harms. Psychologists play a crucial role in these discussions, given their expertise in cognitive biases and cultural inclusion.

Ethical Considerations: Designers can combat bias by using diverse training data, implementing bias-detection processes, creating transparent algorithms, and adhering to ethical standards that prioritize fairness.

Thank you for joining in on this LIVE chat on “AI Ethics and Bias”. I hope you found this chat informative and engaging. Stay tuned for more exciting discussions on the latest trends and developments from The Digital Revolution with Jim Kunkle. This concludes the LIVE chat portion of this session.

OK, have a great evening, afternoon or morning no matter where you're listening from.