Disclaimer: This article was co-authored by human writers and OpenAI’s ChatGPT. At ExperienceIT, we pride ourselves on activating the potential in people and technology. To do this, we strive to use cutting-edge tools like Generative AI optimally, forming industry best practices.
Responsible AI governance is important for organizations that use Artificial Intelligence (AI) technologies. AI Governance AI sole purpose is to ensure its use in an ethical and responsible way. Focusing on AI governance helps us to:
- Prevent privacy breaches
- Stop bias including stereotypes from influencing results
- Ensure people’s jobs become easier, not harder
- Maintains its effectiveness over time
To do this, we need to set benchmarks and guidelines.
Areas to Focus On
When it comes to benchmarking AI governance, focus on the following topis is important:
- Ethical Frameworks and Principles
- Risk Management and Auditing
- Stakeholder Engagement and Collaboration
- Continuous Monitoring and Improvement
By paying attention to these areas, companies can use AI in a way that helps.
Ethical Frameworks and Principles
Like any other tool, you can use AI for good or bad ends. AI can help organizations achieve positive outcomes. Yet, it can also be misused to perpetuate bias, violate privacy, or take credit for others’ work.
That’s why it’s important for businesses to define clear ethical frameworks and principles that govern their AI usage. These should include privacy, fairness, transparency and explain-ability, and accountability.
Privacy concerns who has access to the information, what information is accessible to the AI model, and how that information will be utilized. This is especially important when working in regulated industries, such as healthcare and finance, as irresponsible use of patient and client information is both immoral and likely illegal. Organizations such industries need to make sure their privacy standards are compliant with local, state, federal laws and regulatory standards.
Additionally, employee privacy rights need to be protected. It is an organization’s job to tell employees what data machine learning utilizes and allow opt-out opportunities when appropriate.
Fairness refers to whether AI results are colored by bias or misinformation. Because AI models are trained on human information, human biases cannot be overlooked.
For example, let’s say a healthcare organization is using AI to help prescribe medication to patients. If the AI model is trained on data from primarily male patients, it may suggest incorrectly for a female patient.
Bias detection, fairness testing, and drawing from diverse datasets are techniques that help to promote AI fairness. Companies should take advantage of these to make sure that their AI usage delivers results that are fair and equitable.
Transparency and Explainability
Transparency and explainability go hand in hand. Transparency refers to how easy it is to see what information an AI uses to generate results. Explain-ability refers to how easy it is to understand why an AI prioritized and interpreted that information the way it did.
Having a transparent and explainable AI model is essential for responsible use of this technology. Without both pieces in place, it is nearly impossible to promote safety, fairness, and other ethical values with AI.
AI should not be treated like an “autopilot”. No matter how robust your privacy measures are, how unbiased your model is, or how easy it is to understand, you cannot blindly accept whatever AI generates.
Humans need to bear the ultimate responsibility when it comes to AI results and how they are interpreted and implemented. Processes must be in place to evaluate the results an AI model delivers and implement those results in a way that universally beneficial.
Risk Management and Auditing
Implementation of a new technology is not without risk. Governance and oversight need to be considering as part of the adoption to ensure ethical implications are understood throughout.
That’s why it’s important to benchmark risk management practices. This means evaluating potential biases, security vulnerabilities, and unintended consequences.
Regular audits are important for maintaining these benchmarks. This helps identify risks and areas where we’re falling short of goals. From there, we can take immediate action to fix any security risks.
Stakeholder Engagement and Collaboration
AI doesn’t exist in a bubble. A model’s outputs will affect people throughout our business. That’s why it’s crucial to involve stakeholders and end users to understand how AI is affects them.
Benchmarking stakeholder engagement means including different perspectives. Avoid confirmation bias by only involving key persons from the implementation model such as high-level business architects. You also must ask the people who are working with the model how it impacts them.
Is the AI model making them more efficient? Is it taking work off their plate so they can focus on more important things?
Benchmarks that measure these factors are what separate successful adoptions from unsuccessful ones.
Continuous Monitoring and Evaluation
Using AI responsibly doesn’t end with the initial implementation. Rather, it’s an ongoing process.
We must set benchmarks for continuous AI monitoring and evolve. The assessment should be robust to identify biases, ethical concerns, or unintended consequences. If organizations find an issue, they adapt to resolve them immediately.
Using AI in an ethical and responsible way is important. That’s why benchmarking AI governance is crucial. Organizations can use AI responsibly by setting benchmarks for ethical frameworks, risk management, stakeholder engagement, and continuous monitoring.
If you’re interested in implementing AI in your organization, ExperienceIT can help. Now a part of Globant’s network of AI experts, ExperienceIT can find the AI solution that is right for you. To learn more about the solutions we offer, visit us here.