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With Generative AI Advances, The Time to Tackle Responsible AI Is Now




In 2022, companies had an average of 3.8 AI models in production. Today, seven in 10 companies are experimenting with generative AI, meaning that the number of AI models in production will skyrocket over the coming years. As a result, industry discussions around responsible AI have taken on greater urgency.

The good news is that more than half of organizations already champion AI ethics. However, only around 20% have implemented comprehensive programs with frameworks, governance, and guardrails to oversee AI model development and proactively identify and mitigate risks. Given the fast pace of AI development, leaders should move forward now to implement frameworks and mature processes. Regulations around the world are coming, and already one in two organizations has had a responsible AI failure.

Challenges Implementing Responsible AI

Responsible AI spans up to 20 different business functions, increasing process and decision-making complexity. Responsible AI teams must work with key stakeholders, including leadership; business owners; data, AI, and IT teams; and partners to:

  • Build AI solutions that are fair and free from bias: Teams and partners can use different techniques, such as exploratory data analysis, to identify and mitigate potential biases before developing solutions—that way, models are built with fairness in mind from the start.Teams and partners can also review the data used in preprocessing, algorithm design, and postprocessing to ensure that it is representative and balanced. In addition, they can use group and individual fairness techniques to ensure that algorithms treat different groups and individuals fairly. And counterfactual fairness approaches model outcomes if certain factors are changed, helping identify and address biases.
  • Promote AI transparency and explainability: AI transparency means it is easy to understand how AI models work and make decisions. Explainability means these decisions can be easily communicated to others in non-technical terms. Using common terminology, holding regular discussions with stakeholders, and creating a culture of AI awareness and continuous learning can help achieve these goals.
  • Ensure data privacy and security: AI models use mountains of data. Companies are leveraging first- and third-party data to feed models. They also use privacy-preserving learning techniques, such as creating synthetic data to overcome sparsity issues. Leaders and teams will want to review and evolve data privacy and security safeguards to ensure that confidential and sensitive data is still protected as it is used in new ways. For example, synthetic data should emulate customers’ key characteristics but not be traceable back to individuals.
  • Implement governance: Governance will vary based on corporate AI maturity. However, companies should set AI principles and policies from the start. As their AI model use increases, they can appoint AI officers; implement frameworks; create accountability and reporting mechanisms; and develop feedback loops and continuous improvement programs.

Critical Enablers of a Responsible AI Program

So, what differentiates companies that are responsible AI leaders from others? They:

  • Create a vision and goals for AI: Leaders communicate their vision and goals for AI and how it benefits the company, customers, and society.
  • Set expectations: Senior leaders set the right expectations with teams to build responsible AI solutions from the ground up rather than trying to tailor solutions after they’re completed.
  • Implement a framework and processes: Partners provide responsible AI frameworks with transparent processes and guardrails. For example, data privacy, fairness, and bias checks should be built into initial data preparation, model development, and ongoing monitoring.
  • Access domain, industry, and AI skills: Teams want to accelerate the innovation of AI solutions to increase business competitiveness. They can turn to partners for relevant domain and industry skills, such as data and AI strategy-setting and execution, paired with customer analytics, marketing technology, supply chain, and other capabilities. Partners can also provide full-spectrum AI skills, including large-language model (LLM) engineering, development, operations, and platform engineering capabilities, leveraging responsible AI frameworks and processes to design, develop, operationalize, and productionize solutions.
  • Access accelerators: Partners offer access to an AI ecosystem, which reduces development time for responsible traditional and generative AI pilot projects by up to 50%. Enterprises gain vertical solutions that increase their market competitiveness.
  • Ensure team adoption and accountability: Enterprise and partner teams are trained on new policies and processes. In addition, enterprises audit teams for compliance with key policies.
  • Use the right metrics to quantify results: Leaders and teams use benchmarks and other metrics to demonstrate how responsible AI contributes business value to keep stakeholder engagement high.
  • Monitor AI systems: Partners provide model monitoring services, solving problems proactively and ensuring they deliver trusted results.

Plan for Responsible AI Now

If your company is accelerating AI innovation, you likely need a responsible AI program. Move proactively to reduce risks, mature programs and processes, and demonstrate accountability to stakeholders.

A partner can provide the skill sets, frameworks, tools, and partnerships you need to unlock business value with responsible AI. Deploy models that are fair and free from bias, enforce controls, and increase compliance with company requirements while preparing for forthcoming regulations.

Dr. Ravindra Patil is the Senior Director of Data Science at Tredence, leading a team with 15 years of industrial experience in Data and AI. His expertise lies in successful team leadership and developing effective Data and AI solutions. Ravindra began his career at Siemens, later contributing significantly at Philips Research and its business groups. Before joining Tredence, he led a Data and AI group for Philips' $4 billion Personal Health cluster.

Throughout his journey, Ravindra has created multiple AI algorithms, data platforms, and facilitated their integration into various business sectors. He holds a Bachelor's in Engineering, a Master's in pattern recognition from IIT Madras, India, and a Ph.D. in machine learning from the University of Maastricht, Netherlands. With over 30 patent filings, numerous published research papers, and recognition as one of India's top 100 AI leaders by AIM magazine, his accomplishments are noteworthy.