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Blog | Mar 1, 2024

Responsible AI: 5 Principles for Implementation

Responsible AI
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Labor and skills shortages plus an economic upheaval are leaving many organizations wondering, “What can I do to make my business more efficient while maintaining (or ideally improving) quality and customer/employee experience?” Many are turning to artificial intelligence (AI) to fill those gaps and aid in more effective decision-making processes.

With AI constantly creating new opportunities, it also raises questions about the ethics of the technology: fairness, equality, privacy, transparency, trust and control.

In this article, we’ll look at:

  • What responsible AI is and how to mitigate the risks.
  • The benefits of implementing responsible AI automation.
  • Best practices when you develop and deploy an AI solution.
  • Our five core principles to responsible AI adoption.

What Is Responsible AI?

Responsible AI is about approaching the AI development process from an ethical and legal standpoint. The goal is to employ safe, trustworthy, transparent AI usage. This protects you by ensuring your organization stays compliant and follows all the applicable laws and regulations; it also ensures your organization maintains a good reputation. AI systems should treat people, security and the environment with the highest standards at all times.

  • AI governance should include AI model documentation and auditing pipelines to show how your AI is trained and tested and how it behaves throughout its lifecycle.
  • AI compliance ensures your AI-powered systems adhere to any applicable laws or regulations and can include data privacy and intellectual property.

Good AI is good for your business. That includes:

  • Accountability: Evaluating how your AI systems perform – intended and unintended – is critical to building accountable products and services.
  • Trust: If you can’t trust your AI systems, it creates a barrier to adopting further technologies across your enterprise. A responsible approach earns trust and loyalty.
  • A culture of responsibility: You need to empower the decision-makers and developers in your business to consider the ethical side of AI adoption and enable them to find innovative ways to drive success without sacrificing responsible practices.

Where does automation fit in?

Intelligent automation (IA) combines robotic process automation (RPA), business process management (BPM) and AI to automate and streamline business processes. AI is a key factor in intelligent automation, which is why responsible AI is so imperative. How you’re training and adapting your IA initiative will directly affect your organization’s reputation and adherence to laws and regulations.

What are the risks of AI?

If you don’t take a responsible approach to leveraging AI, you may run into legal, financial and reputational penalties. The data sets you use to train your machine learning (ML) models can introduce bias into your AI system. This can be caused by faulty or incomplete data.

If aspects like data quality aren’t considered when training models, this can result in a biased AI program, which can poorly affect people and businesses. Irresponsible AI adoption runs the risk of:

  • Errors or incorrect data
  • Bias and discrimination
  • Lack of interpretability
  • Performance instability
  • Inability to scale
  • Costly penalties and fines

So, how can AI be used responsibly?

Create more transparent AI systems to increase end-user trust and protect your people and organization from negative repercussions. Because at the end of the day, if you’re using technologies like AI correctly, you’ll have the confidence you need to scale and develop even better processes across your enterprise.

In the next section, we’ll show you some best practices and tools to help you implement responsible AI standards.

What Are Responsible AI Practices?

Beyond your general best practices for implementing any new software system into your business units, let’s look at some key approaches for ensuring AI ethics.

Before deploying AI into your real-world business practices, start with:

  • Clearly defined ML and AI models.
  • Checking the reliability and safety of your training data.
  • Explainable AI showing how and why your AI got its output.
  • Managing fairness by addressing AI bias and discrimination.
  • Enforcing privacy practices to safeguard data.
  • Looking for potential threats such as adversarial attacks.
  • Building a secure and resilient AI system.
  • Continuously validating and monitoring AI performance by adding a human to the loop.
  • Ensuring safety on all levels, looking at regulatory factors.

How can I prepare for generative AI?

Before implementing emerging technologies such as generative AI, you should develop a strategic plan. Our 2022 survey of 850 senior executives globally revealed widespread recognition of the importance of responsible AI and AI regulation. But only 6 percent of organizations felt they had a fully robust, responsible AI foundation in place. (Accenture, “A new era of generative AI for everyone)

Here are our top tips for how to prepare for gen AI:

  1. Reframe your thinking by looking at gen AI’s potential and threat level.
  2. Educate your business and people in AI-human collaboration and validate content by maintaining human oversight.
  3. Identify gen AI’s strengths, limitations and ethical considerations.
  4. Use quality training data.
  5. Ensure alignment between the business and IT, ensuring governance is both a top-down and bottoms-up approach.
  6. Consider the impact, looking at environmental, social and governance (ESG) factors and the energy usage of training gen AI models.
  7. Find specific gen AI use cases within your organization.
  8. Use suitable AI tools that fit your business requirements and prepare your infrastructure.
  9. Establish AI governance and AI compliance with a responsible AI framework.
  10. Test and train, and test some more; verifying good practices is a continuous operation.

5 Principles of Responsible AI

5 Principles of Responsible AI

Now, let’s design responsible AI principles for your framework. Ongoing monitoring is critical to ensuring your AI stays ethical. Following a maturity model or operating model may help you design and implement a secure and reliable AI system.

At its foundation, you should build your AI around these five mandates:

1. Fairness and inclusiveness

An AI system should treat everyone fairly and without bias toward or against certain groups of people.

For example, when a healthcare provider uses AI to recommend a medical treatment based on a patient’s symptoms, AI should make the same recommendation for anyone with those symptoms regardless of their financial or professional standing.

AI decision-making systems risk unfair bias when based on historical human data and impact. You should ensure that after you’ve trained your ML models on existing data collected from the real world, you identify the problematic biases. These biases may be based on ethnicity, gender, religion, political leaning, culture, etc., and cannot go unnoticed in your AI’s learning.

In practice, these are the factors you should consider in your AI systems:

  • Gather diverse intel from experts.
  • Consider the various use cases of your system and what might be missing.
  • Set goals for your system to work across use cases and monitor those goals over time to see if they are fair and inclusive.
  • Design your algorithms to reflect your fairness goals.
  • Continuously check the system for unfair biases.
  • Evaluate real-world scenarios across a spectrum of users, use cases and contexts.

2. Privacy and security

From business information to personal privacy, the data used by AI is becoming increasingly complex. Training data can be sensitive and, in some cases, important for your AI system (e.g., cancer detectors based on past patient scans).

Your AI system needs to comply with all laws and regulations applicable to your region and industry. But they should also consider social and individual expectations. Particularly, look at the transparency of:

  • How you collect, use and store data.
  • Only collect and use the required amount of data for the intended purpose.
  • Permission for using customer or employee data in your system.
  • How you communicate data usage.
  • User access across your business.
  • Data encryption and exposure.
  • Any incoming or outgoing network communications.

3. Transparency

To have an effective AI model, you need direct insights into how everything in your system runs. You should know exactly how your AI system makes a decision, called “explainable AI”. This helps you avoid mistakes or biases and also supports any scaling you’d like to do in the future.

For example, if a bank uses AI to decide if a person is eligible for a loan, the decision-makers should be able to go back through the data and see exactly how the system arrived at this choice, and whether any unexpected biases were present.

Transparency will show you:

  • How the model was trained on the data and algorithms.
  • What logic was applied to the data.
  • The final model generated by the AI system.

4. Accountability

The developers designing and deploying your AI system are accountable for any action or decision it makes. Establish an internal review body early on to give your systems and people the guidance and oversight they need to help your organization thrive. Draw upon industry standards to help you develop accountability norms and ensure your AI systems aren’t the final authority on any decision.

Look for an ML training model that enables you to:

  • Track all metadata used within the model.
  • Audit end-to-end decisions.
  • Notify users of skewed results or potential biases.
  • Gain data-driven insights showing outcomes.

5. Reliability and safety

Consistency is key to a reliable and safe AI system. It should perform as it was originally designed and also respond safely in new situations. How your systems behave and respond to unanticipated conditions depends on the range of conditions set during the design and testing phases.

For example, discrepancies may occur when your system underperforms for a certain demographic. That’s why rigorous testing and validation are imperative. Developers need to check how your AI system responds to edge cases within your operating conditions.

It’s also important to note that your AI system performance can degrade over time. Continuous monitoring and model tracking are essential to keep your AI’s performance reliable and safe.

In practice, your developers need to:

  • Understand how failure is distributed for a model.
  • Identify subsets of data with higher error rates.
  • Find and eliminate AI bias before you scale.

What Are the Benefits of Responsible AI?

Benefits of Responsible AI

From the purpose of the system to how people interact with it, your AI adoption should have beneficial and equitable outcomes. To achieve those benefits, you need a robust, strategic AI framework.

With responsible AI practices, you can:

  • Minimize unintended biases: Develop responsible algorithms that ensure your AI’s underlying data is as unbiased and representative as it can be.
  • Establish trust and transparency: Create explainable AI that’s transparent across all processes and functions to ensure trust among customers, stakeholders and employees.
  • Protect privacy: Ensure the best privacy and security for your data so all personal and sensitive information is never used unethically or without permission.
  • Give employees opportunities: Ask people within your business to flag any doubts or concerns they may have with the AI system and encourage innovation.

Automate Responsibly

Overlooking AI ethics and best practices can result in financial and reputational damage for your organization. But with responsible AI, your organization can scale and evolve all of its operations.

Central to responsible AI are five core principles: fairness and inclusiveness, privacy and security, transparency, accountability, and reliability and safety. Upholding these principles requires clear ML and AI models, rigorous data validation, explainable AI systems, bias mitigation strategies, robust security measures, and continuous monitoring and validation.

Responsible AI isn’t just a moral imperative; it’s a strategic necessity for organizations navigating the complexities – and the benefits – of an AI system. As you embrace the transformative power of AI, do so with a commitment to responsible innovation, ensuring that technology serves as a force for good in our interconnected world.

Download our Gartner® report: A CTO’s Guide to the Generative AI Technology Landscape

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