Ethical management in the adoption of generative AI: A guide

Generative AI – every business wants to adopt it, but very few of them are sure of it. The most common question that every enterprise has – how safe is my data? How to ethically manage generative AI? What can I do to minimize risk in data security and vulnerabilities?

As in most technology adoptions – this can be managed by setting up a governance plan. Simply put, work on policies, processes, framework, and regulations to adhere to, set up independent review committee, assess, implement, and mitigate risks. 

Before developing an assessment framework, understand the key requirements to plan Generative AI governance: 

1. Algorithm: Guarantee that GenAI algorithms are designed to minimize biases, errors, and potentially harmful outputs 
2. Data: Verify that the data used to train GenAI models complies with data privacy regulations
3. Usage: Make sure you understand how GenAI is utilized, establish, and uphold guidelines and policies for usage, security, privacy, and compliance

Identifying risks and evaluating the key factors to minimize them 

The most important factor to assess includes – conceptual relevance. Is it viable theoretically? What has the proof of concept yielded? What are the model merits and limitations?

Once you’ve defined these, check the data quality, its relevance, and diversity.  

• Data Quality: Clean, accurate, and representative data trains GenAI for reliable responses, and ensures integrity. Flawed data can lead to misleading responses
• Data Diversity: Diverse datasets mitigate biases in GenAI that may emerge from limited or skewed data representations, enabling effective responses to a wide range of inquiries 
• Bias and Fairness: Non-diverse training data biases GenAI responses. For example, if the data primarily represents a particular demographic or perspective, it may struggle to understand or respond appropriately to individuals outside of that demographic. This can lead to unfair or discriminatory treatment
• Privacy and Security: Customer support often involves sensitive information. Ensuring that GenAI handles this data in a secure and compliant manner is crucial to maintain trust and protect individuals' privacy
• Transparency: Customers have a right to know if they are interacting with an AI system rather than a human. Providing clear disclosure of this information is important for transparency and trust
• Accountability and Error Handling: GenAI can err, so accountability and error correction are vital. Establish avenues for human agents to intervene
• Continual Monitoring and Improvement: Regularly reviewing and improving the GenAI system is essential to identify and rectify any biases, errors, or shortcomings that may arise over time

Assurance of data privacy and protection while leveraging Gen AI

Confidentiality, leakage prevention, and reliability – these are three crucial aspects to ensure that the data we feed is secure. Check how is the model/your tech partner working to ensure that the data has no risk of exposure of private information. Is the data in the platform reliable? How are anomalies detected and fixed? While assessing these, also ensure to check for legal implications, social impact, and business reputation. A holistic platform management process should include all these checkpoints.

Identification of security vulnerabilities in Generative AI infrastructure  

Generative AI (GenAI) infrastructure security requires a multifaceted approach that addresses its intricate architecture. Adversarial resilience, understanding model inner workings, data privacy, bias detection and mitigation, and transparency and explain-ability are key pillars.

Anticipating unforeseen threats is paramount in the dynamic cybersecurity landscape. Vigilance for zero-day vulnerabilities, adoption of best practices, red teaming, and penetration testing strengthen the Gen AI ecosystem. Collaborative knowledge-sharing within the AI community fortifies the collective defense against emerging threats.

Handling bias in Data and reduce in outcome 

Combatting bias requires a finely orchestrated symphony of technical strategies. Adversarial debiasing techniques inject a dose of sophistication by pitting a discriminator network against the generative model, effectively training it to distinguish between biased and unbiased examples. This adversarial feedback loop enforces a continuous process of self-improvement, gradually reducing the influence of inherent biases. By assigning greater weights to underrepresented groups or samples, the training process becomes more balanced, mitigating the dominance of majority groups and fostering a more inclusive model. By encoding demographic parity or equal opportunity constraints into the loss function, the model is guided towards generating outputs that exhibit greater equity. Counterfactual explanations serve as a beacon of insight, enabling a detailed analysis of bias sources. 

A multifaceted technical framework empowers Generative AI to produce outputs that are not only technically proficient but also ethically sound and socially responsible. 

The article has been written by Muthu Chandra, Director, AI Engineering, Ascendion