What Is Ethical AI?

Ethical artificial intelligence (AI) is the set of principles and practices that promote the responsible development and deployment of AI systems. As with any new technology, AI systems have a transformative impact on users, society, and the environment. Ethical AI means taking steps to enhance positive impact, as well as prioritizing fairness and transparency around how AI is developed and used. Ethical AI ensures that AI innovations and data-driven decisions avoid infringing civil liberties and human rights.

With ethical AI, organizations can address the following bias, explainability, and data privacy concerns associated with generative AI and other machine learning (ML) models: 

·       Bias is a phenomenon where the AI system favors a specific group of users.

·       Explainability describes whether computer science experts can reason why and how an AI model makes a decision.

·       Data privacy concerns address proper data usage and security measures to safeguard user privacy. 

Organizations must comply with data privacy laws and safeguard customer privacy when using AI. 

Generative AI technologies learn from training datasets and analyze information with multiple hidden layers of neural networks. Because of its complex architecture, generative AI may produce unfair outcomes that engineers cannot explain. For example, an insurance chatbot trained with incomplete data may favor specific demographics when approving submitted claims. Ethical AI principles allow you to establish frameworks to govern data usage when training and deploying AI models. 

The following are some benefits to ethical AI practices.

Customer trust

Customers can mistrust generative AI systems that they don’t understand. Lack of transparency regarding training data can further aggravate ethical challenges that companies face. Conversely, you can build a positive brand image for your business if you are transparent about your system's:

·       Capabilities and limitations.

·       Method of operation.

·       Data processing, storage, and handling policies.

For example, customers trust the recommendations an AI-powered virtual assistant provides when they know how the AI model makes a decision. 

Employee awareness

Apart from AI algorithms or software procedures, data engineers, labelers, and AI experts can also unwittingly add bias in AI technologies. This happens when AI teams rely on their judgment when fine-tuning a large language model for industry-specific applications. Ethical AI practices can increase organizational awareness and empathy, helping your teams make better decisions. For example, an AI development team should include representatives from diverse demographics to consider all perspectives when choosing a dataset.

Regulation compliance

Government regulations address data ethics and privacy concerns and ensure companies fulfill their legal responsibilities. Ethical AI practices help companies comply faster with new laws regulating AI technologies. For example, data engineers anonymize personally identifiable information when training a large language model for medical AI applications. Then, they use encryption to secure the data the model uses to comply with relevant AI regulations. 

Sustainable innovation

Incorporating ethical AI principles helps companies remain competitive as AI adoption increases. Ethical AI usage aligns organizational goals with stakeholder interests while accelerating new AI technologies to market. For example, evaluating AI models for bias allows organizations to streamline generative AI systems with fairness and moral principles. 

Ethical AI allows organizations to overcome ethical concerns that could prevent AI adoption at scale. The following are some principles to consider in implementing ethical AI. 

Beneficence

AI systems should have beneficial outcomes for individuals, society, and the environment. Even AI systems used for legitimate internal business purposes, like increasing efficiency, can have broader impacts on the outside world. For example, medical AI applications should benefit not only patients but also the broader community by providing factual information. 

Accountability

Creators and owners of AI systems are responsible for the human oversight, impact, and ethical implications of that system. Organizations should put mechanisms—such as legal requirements and external reviews—in place to ensure accountability in the design, development, deployment, and operations of all AI systems. For example, lawyers should be aware of the risks of model hallucination when using generative AI to assist their research. 

Transparency

Transparency requires responsible disclosure of the use and impact of AI systems. When done appropriately, transparency ensures that:

·       Users know when and why they are engaging with AI systems.

·       Data scientists understand why and how the system processes data.

·       Developers and engineers can make changes responsibly.

·       Regulators and investigators can inform evidence and decision-making.

·       Technology companies disclose how they curate data for training AI models. 

In this way, the general public can gain confidence and trust in the generative AI technologies. 

Fairness

AI systems should provide fair and unbiased responses to everyone, regardless of race, gender identity, ethnicity, or other demographic traits. Systems can achieve the principle of fairness through:

·       Consultation with diverse stakeholders to ensure equitable access and treatment.

·       Consideration of the system's impact on vulnerable and underrepresented groups.

·       Compliance with anti-discrimination laws.

·       Training AI models with diverse and inclusive datasets. 

·       Applying explainable AI frameworks to understand how a deployed generative AI model works.

Respect

All AI systems should respect and promote basic human rights. AI systems should serve human interests and not the other way around. AI systems should stay aligned with their disclosed purposes.

Ethical use of AI systems ensures that all users have full control over themselves and their decisions. You can achieve this by adopting ethical AI policies that:

·       Support an equitable, democratic society.

·       Respect human freedom and individual autonomy.

·       Address risks to human dignity.

·       Consider diverse perspectives.

Collaboration

AI development should be an inclusive process, considering the views of different stakeholders. A narrow approach in developing AI applications might result in biased models because of limited perceptions when training the ML algorithm. Alternately, setting up a diverse team of business analysts, ML engineers, and industry experts can help organizations develop fairer AI systems. 

You can take a more collaborative approach to building ethical AI systems by:

·       Encouraging knowledge-sharing among academics, governments, AI experts, business leaders, and other stakeholders.

·       Engaging the public through open dialogue on safe and ethical AI usage.

·       Involving employees, customers, and business managers in efforts to scale AI across various use cases. 

By adopting the following best practices, organizations can develop AI systems that consider user privacy, legal requirements, social implications, and human values.

Define ethical AI policies

Support AI adoption with clearly defined goals, policies, procedures, and frameworks. From training machine models to scaling generative AI across different use cases, establishing clear guiding principles enables you to apply transparency, privacy, and fairness throughout the AI lifecycle. These principles help you choose AI tools, datasets, and development processes that align with AI regulation and human ethics.

Enforce AI ethics governance

Set up an AI ethics committee to oversee AI implementation. The committee should include ML engineers, business managers, data scientists, and compliance teams to govern initiatives in deploying ethical AI. Strong AI governance ensures all parties are responsible and accountable when using AI. 

Involve humans in AI development

Generative AI requires human involvement to produce optimal results. Practice the human-in-the-loop approach when training, deploying, and repurposing AI for business-specific applications. Human-in-the-loop is a technique that augments machine learning training with human feedback. For example, you can engage medical experts to assess and fine-tune a healthcare chatbot to ensure it demonstrates helpful, accurate, and unbiased results. 

Apply risk management 

Use a risk-management framework to assess potential risks in different AI models. Generative AI is an evolving technology that needs continuous improvement to ensure safer and more ethical AI usage. Consider probable outcomes when using a specific AI technology and apply safeguards to mitigate potentially unintended consequences. 

Audit and refine AI systems

Regularly audit AI systems to ensure they perform according to specified ethics guidelines. Evaluate AI models against safety performance benchmarks, such as truthfulness, bias, and toxicity. Communicate necessary changes to engineers, data scientists, and business teams to maintain human-aligned principles for all deployed AI applications. 

AWS offers a comprehensive range of AI and machine learning services that you can use to innovate faster without compromising your ethical principles and standards. For example, you can:

·       Detect bias across the entire ML workflow to improve your model's fairness and transparency.

·       Access a comprehensive set of security features that support a broad range of industry regulations.

·       Generate explainability reports so stakeholders can see how and why models make predictions.

Amazon Augmented AI helps you conduct a human review of ML systems to guarantee precision. When you use Amazon AI services such as Amazon Rekognition, Amazon Textract, or your ML models, you can use Amazon Augmented AI to get a human review of low-confidence predictions for bias avoidance.

Amazon SageMaker Ground Truth offers the most comprehensive set of human-in-the-loop capabilities, allowing you to harness the power of human feedback across the ML lifecycle to improve the accuracy and relevancy of models. From data generation and annotation to model review and customization, you can complete various human-in-the-loop tasks with SageMaker Ground Truth, either through a self-service or an AWS-managed offering.

Amazon Bedrock is a fully managed service that offers a choice of high-performing foundation models (FMs) through a single API, along with a broad set of features to build generative AI applications with security, privacy, and responsible practices. It is compatible with common compliance standards including GDPR and HIPAA. With Amazon Bedrock, your content is not used to improve base models and is not shared with third-party model providers. You can use AWS PrivateLink with Amazon Bedrock to establish private connectivity between your FMs and on-premises networks without exposing your traffic to the internet.

Get started with ethical AI on AWS by creating a free account today.