by Chuck Gallagher
What is the hottest business topic this year? No, it’s not interest rates or an impending recession; it’s artificial intelligence. A recent survey indicated that 67 percent of IT professionals believe that generative AI will be a priority for their company within the next year and a half, despite ongoing concerns about generative AI ethics and responsibility. Even those who believe generative AI is “overhyped” believe the technology will improve customer service, reduce workloads, and increase organizational efficiencies.
As businesses race to adopt generative AI, the challenge is mitigating risks. Ethics, bias, transparency, privacy, and regulation provide flash points to consider when applying generative AI in our business environments.
Taking a responsible approach to AI and its implementation opens the door for businesses to maintain trust with their customers, employees, and stakeholders. The currency of business is trust. With it, brands can thrive as revenue declines and employees leave. And once broken, trust is difficult to reestablish.
That is why it is critical to preserve trust before it is broken.
Here are some proactive ways to maintain confidence in generative AI implementations.
Reducing bias and unfairness
Fairness and bias reduction are critical components of responsible AI deployment. Bias can be introduced unintentionally by AI training data, algorithms, and use cases. Consider a multinational retailer that uses generative AI to personalize customer promotional offers. The retailer must avoid biased outcomes such as only offering discounts to specific demographic groups.
To do so, the retailer must collect diverse and representative data sets, use advanced bias detection and mitigation techniques, and implement inclusive design practices. How, one might ask? We’ll continuously monitor the systems in us to help ensure effective use.
Creating transparency and explanation
Transparency and explainability in AI models are critical for establishing trust, ensuring accountability, and mitigating bias and unfairness. Consider a company that uses generative AI to predict claim amounts for its policyholders. When policyholders receive their claim amounts, the insurer must be able to explain how they were calculated, making transparency and explainability essential.
Because of the complexity of AI algorithms, achieving explainability, while necessary, can be difficult.
On the other hand, organizations can invest in explainable AI techniques (such as data visualization or decision trees), provide thorough documentation, and foster an open communication culture about AI decision-making processes.
These efforts contribute to de-mystifying the AI system’s inner workings and promote a more responsible, transparent approach to AI deployment.
Protecting your privacy
Another critical consideration for responsible AI implementation is privacy. Consider a healthcare organization that uses generative AI to forecast patient outcomes based on electronic health records.
Individual privacy must be protected as a top priority. Generative AI may unintentionally reveal sensitive information or generate synthetic data that resemble real people.
AI does not excuse violations of law and regulations. Businesses can address privacy concerns by implementing best practices such as data anonymization, encryption, and privacy-preserving AI techniques such as differential privacy. In addition, and expected, compliance with HIPPA and GDPR.
Regulatory Requirements and Expectations
The landscape for AI is evolving, and so is the regulatory landscape. And, likely, the regulatory landscape will always be behind in creation as, likely, AI issues will help frame regulatory applications moving forward. It’s incumbent
The regulatory landscape for AI is evolving. As a result, there must be a
solid governance framework to guide ethical and responsible AI deployment.
When you adapt to changes in regulations and proactively address potential risks, an organization can demonstrate their responsible AI practice commitment. When the organization establishes AI ethics committees and fosters the development or monitoring of AI systems, they can embrace regulatory changes proactively.
It’s a brave new world, and while AI has the potential for tremendous advancements in business and beyond, the mystery surrounding its power makes effective implementation difficult. One thing is sure: AI will alter the landscape of many business operations and spark healthy debate about ethics and effective implementation.
About Chuck Gallagher
Chuck Gallagher is an international speaker on ethics, business development, and AI. He has spoken to organizations worldwide and is known for connecting the dots between behavior and outcome. Recently, his AI focus has him speaking and writing for audiences in business, healthcare, government and expanding marketing organizations. Chuck is a certified speaking professional and the author of 5 books.