Rapid advances in AI have created exciting opportunities for task automation and cost-cutting. Yet the technology also has the potential to introduce biases, infringe on human rights, and give misleading results. As a result, individuals and global names alike are pushing for standards to ensure more ethical use of AI.

Research shows that just 35% of global consumers trust AI implementation in organizations, and 77% believe companies should be held accountable for any AI misuse. It’s easy to see why, when Clearview AI facial recognition technology was sued for collecting and selling personal data without consent, human resources algorithms can exclude people of color from hiring, and Instagram’s algorithm has even been accused of promoting nudity by favoring posts with more skin exposure.

With businesses increasingly relying on AI to make decisions, the challenge for companies is to avoid building algorithms that promote bias, harm individuals, and lead to reputational and financial damage.

This article will explain how to build ethical AI solutions and address the potential risks with real-life implementation examples.

But first, let’s start by defining ethics in AI.

What Is Ethical AI, and Why Should Your Business Care?

Ethical AI refers to the development, deployment, and reliable and fair use of artificial intelligence technologies. This concept is tightly related to a responsible AI. Responsibility standards enable the development of ethical systems that work as intended, taking into account the moral impact on individuals, society and the environment.

Responsibility standards enable the development of ethical solutions that act as intended and consider impact on individuals, society, and the environment.

Ethical artificial intelligence aims to:

  • Avoid biases and discrimination
  • Maintain transparency and accountability
  • Respect individual privacy and autonomy

In this context, there are several reasons why your business should consider adopting ethical AI practices.

  • Mitigate legal and regulatory risks. Many countries are adopting regulations like the EU AI Act, which control AI and data privacy. Integrating ethical AI early in product development can help your business stay compliant and avoid future legal and financial issues.
  • Avoid bias and discrimination. Unconscious bias in AI algorithms can contribute to perpetuating social inequalities. Ethical AI practices help identify and mitigate such biases to ensure your company is offering fair and equal opportunities, for example, when hiring and evaluating performance.
  • Responsible data management. Ethical AI product development helps ensure you handle data responsibly and securely through thorough testing, monitoring, and ongoing evaluation.
  • Building trust and reputation. When people know that your AI systems are designed to treat them fairly and responsibly, they are more likely to engage with your products and services.
  • Long-term viability. Consumers are becoming increasingly conscious of ethical considerations, and showing awareness can give your business a competitive advantage over the longer term.

Now that we’ve covered why ethical AI matters, let's learn how your company can navigate the complex landscape of AI responsibly.

Ways To Ensure AI Ethics

In one of our articles, Techstack outlined core practices for ensuring fairness in ML systems. Proper data collection, ML model development, and training are essential for ensuring algorithmic accountability, explainability, and fairness. Yet, in addition to monitoring product metrics, it’s also important to consider the social impact of your AI initiatives.

To effectively implement ethical AI principles, you need to embed ethics in the design and development process from the beginning of AI product creation.

Here’s how to ensure the ethical use of AI throughout the development process.

  • Clearly define the AI task and objectives. From the outset, outline the problem you want to solve, the desired outcomes, and the potential impact your AI system will have on individuals and society. You need to consider the ethical implications of using the system and identify potential biases or discrimination it may lead to.
  • Prioritize ethical considerations alongside technical and business goals. Consider the culture behind the values and assumptions that shape your development goals. To increase diversity, involve stakeholders, domain experts, and representatives of relevant user communities in your prioritization process.
  • Choose evaluation metrics that align with ethical values. Consider fairness, transparency, and privacy when selecting metrics to ensure your AI outputs meet ethical standards. Using several metrics will allow you to learn the tradeoffs between different types of errors and experiences more effectively.
  • Gather data responsibly and transparently. Ensure that the data you use to train and test your AI system is diverse, representative, and free from biases that could influence the system's behavior. You’ll also need to clearly communicate the data collection process to users and obtain approval when necessary. Otherwise, you may end up like Alexa, which was sued for collecting user voice data without consent.
  • Eliminate biases and prejudices in data collection. Using a wide range of data sources will ensure you represent different groups, genders, ethnicities, and socio-economic backgrounds in your training data. Additionally, you can form diverse and inclusive data collection teams to ensure a broader perspective and minimize unintentional bias.
  • Use pre-, in- and post-processing algorithms. Data pre-processing involves thoroughly cleaning data to remove noise, personally identifiable information (PII), and inaccurate or inconsistent sources. In-processing algorithms are useful in cases where bias is not easily recognizable, or the dataset is too large to pre-process. Post-processing methods modify the output of the model.
  • Train AI models. Use techniques like adversarial training and fairness constraints during model training to ensure your AI system does not favor or discriminate against any specific group. Regularly monitor the model's performance and impact to ensure ethical alignment.
  • Interpret the results. Use interpretable (glass-box) AI models to understand the algorithm's decision-making process. Implement explainability best practices like Local Interpretable Model-agnostic Explanations (LIME) or SHapley Additive exPlanations (SHAP) to gain insights into individual predictions and model behavior.
  • Perform regular ethical reviews. Audit your AI system regularly to verify it’s operating and making decisions within your desired ethical standards. Consider how the AI system might impact vulnerable or marginalized groups, and make sure it doesn’t increase existing inequalities.
  • Incorporate user feedback and establish accountability. Encourage users to share thoughts on using your AI solution to improve its ethical performance and address unintended consequences. Clearly define the outcomes your team is responsible for and monitor their results.

These steps will help you steer your ethical initiatives in the right direction. To put your strategy into practice, you will also need a reliable framework to assess the ethics of machine learning algorithms in your system at every stage of development.

Frameworks To Track and Ensure Ethicality

Frameworks aim to identify potential ethical challenges and suggest ways to overcome them or mitigate the associated risks. An AI ethical framework gives guidance in four basic areas:

  • Notions
  • Ethical principles and values
  • Concerns
  • Remedy (strategies, rules, and guidelines)

Here are several frameworks that can help you address concerns related to privacy, fairness, transparency, accountability, and bias in AI systems.

AI4People - An ethical framework for a good AI society

Developed by a group of experts in different fields, this framework offers a comprehensive set of ethical guidelines for European systems. It presents five ethical principles companies must adhere to address AI's societal impact:

  • Beneficence
  • Non-maleficence
  • Autonomy
  • Justice
  • Explicability

The AI4People framework also offers 20 concrete recommendations to help companies maximize the opportunities and minimize the risks inherent in AI systems development.

AWS Guidelines for the Responsible Use of Machine Learning

The AWS guidelines offer insights and suggestions for the ethical and responsible development and use of ML systems throughout the three stages of the system lifecycle: design and creation, implementation, and continuous usage.

For each phase, the guidelines describe use cases, ML capabilities and limitations, and give actionable tips for ensuring ethical data collection and use.

Deloitte’s Trustworthy AI framework

Ethical AI advocate Deloitte has released its own framework to address the ethical challenges of the technology. The organization’s Trustworthy AI Framework addresses essential aspects of ethical AI usage, such as crisis management, effective operation, and responsible models.

The framework also explains how to ensure that a system’s algorithms, attributes, and correlations are open to inspection and guarantee a transparent decision-making process.

The Ethical Application of Artificial Intelligence Framework

Released by the American Council for Technology-Industry Advisory Council (ACT-IAC), this framework suggests how organizations should address bias, fairness, transparency, responsibility, and interpretability in AI systems. It offers best practices for evaluating the integration and impact of each component throughout the project lifecycle.

The framework also has EAAI Scorecard — a tool that helps evaluate AI systems' ethical and social implications.

The EU’s Ethics Guidelines for Trustworthy AI

The European Commission has published its own guidelines for ensuring that AI is human-centric, trustworthy, and technically robust. The guidelines propose seven essential conditions that AI systems must meet to be considered ethical and trustworthy. They also include an assessment list to check if each key requirement is fulfilled.

These are just a fraction of the many AI ethics frameworks emerging to back trustworthy AI development. Different industries and regions may also have specific ethical guidelines tailored to their challenges and contexts.

Choosing the right framework and a development partner with expertise in AI development services can help you create a more ethical AI solution. Yet equally important for a product’s success is promoting an ethics-aware culture within your team. Let’s find out why.

The Role of Ethical AI and ML Understanding Within Your Organization

One of the greatest and often overlooked sources of risk is a lack of understanding of ethical AI principles within teams.

Creating cross-organizational awareness requires consistent work and clear communication of why data and AI ethics matter. Here are some things you can do.

  • Establish ethical guidelines. Develop ethical guidelines specific to your AI project and organization, covering essentials like fairness, transparency, privacy, and bias mitigation. Present them to your team and ensure they adhere to these guidelines throughout development.
  • Conduct regular ethics training sessions and workshops. Invite experts in the field to lead discussions on ethical considerations and best practices. Share and analyze relevant ethical AI case studies and examples from your industry to illustrate the impact of AI decisions on individuals and society. This helps team members understand the possible consequences of their work.
  • Encourage diverse perspectives. Foster an inclusive environment where team members feel comfortable discussing ethical concerns openly. Involve experts and people from different backgrounds and disciplines to bring insights to teams. For instance, Microsoft established a community of Responsible AI Champs—leaders who raise awareness of AI ethical risks across teams.
  • Promote continuous learning about ethical AI and ML. Provide resources such as articles, research papers, and online courses that team members can use to deepen their understanding of ethical AI principles.
  • Build team awareness. Clearly express why data and AI ethics are important to the organization, showing that the commitment goes beyond just a public relations effort.

Now let's explore how tech companies put these principles into practice and integrate ethics into their AI solutions.

Ethical AI and ML in Product Development: Real-World Examples

AI ethics is much more than an abstract concept or idea. Both mature companies and startups take ethical considerations seriously by incorporating accountability, transparency, and explainability into their responsible machine learning algorithms. A database of vetted, ethical AI companies (EAIDB) is constantly growing and includes over 250 names as of August 2023.

Let’s take a closer look at some companies that are putting AI ethics into practice.

Amazon Web Services (AWS)

Amazon initially lagged behind competitors in its promotion of responsible AI practices. Today, however, AWS takes great pride in its commitment to creating AI/ML services that are fair and accurate.

Last year, the company introduced AWS AI Service Cards — documentation on responsible AI design choices, use cases and limitations, deployment, and performance optimization best practices. In addition, AWS provides education and training programs like the AWS Machine Learning University.

Ericsson

Swedish multinational telecommunications company Ericsson is also consciously building ethics into its AI projects. The company is confidently progressing towards creating a fully cognitive network by 2030. This network will be able to learn, reason, and act on business intent with almost autonomous capabilities.

So far, Ericsson adopted EU Ethics Guidelines for Trustworthy AI as part of the company’s agenda to make their AI network lawful, ethical, and robust from a technical perspective.

Gretel

Ethical AI company Gretel generates accurate and safe synthetic data that other AI companies can use to train models. The company solves the data bottleneck problem by providing companies with safe, fast, and easy access to data without compromising accuracy or privacy. Gretel data has been trusted by market giants such as Bayer, SAP, and Riot Games.

Global Copyright Exchange (GCX)

GCX has rocked the music world as the first AI music generation platform for copyright-cleared and ethically sourced music content. Released by music licensing agency Rightsify in spring 2023, GCX offers a complete and compliant dataset licensing framework. Developers, musicians, and entertainment companies can use this framework to train text-to-music and other generative AI ethically.

The company prioritizes the ethical use of data and strives to create a sustainable ecosystem for AI-generated music. GCX has already licensed several well-established tech companies and continues cultivating an ethical, worry-free approach to AI music training.

All these ethical initiatives mark a positive step for the AI industry. Nevertheless, AI implementation around the world is still struggling with moral dilemmas and bias. Recent layoffs of AI ethics staff in big tech have also raised safety concerns. Does this mean the future of ethical AI is under threat?

Risks and Perspectives

Accenture predicts that language-based AI could impact up to 40% of all working hours. In the same source, 98% of business leaders agree that AI foundation models will be crucial to their organization's strategies in the next three to five years.

As we’ve seen, however, the risks are high.

Bias and discrimination, privacy concerns, and lack of transparency are among the major consequences of unethical AI usage. While ChatGPT has captured the world's attention, it has also caused numerous scandals. In spring, the chatbot fabricated a source and accused a prominent law professor of sexual assault. This and many similar cases prove that AI systems lacking human oversight can behave unexpectedly and make decisions with unintended consequences.

In March 2023, over 33,000 leading AI researchers, engineers, and CEOs, including Steve Wozniak and Elon Musk, signed an open letter calling all AI labs to pause developing and training AI systems more powerful than GPT-4 for at least six months. During this period, they want to join forces and create a set of shared safety protocols for advanced AI design and development.

But is it really possible to cover machine learning ethics risks and design a human-centric AI system?

The answer is yes. Most ethical risks can actually be addressed at the model development stage. Since there is still a risk of unethical use by malicious actors, you need to develop a clear vision of your company's ethical principles and create a governance framework to ensure that your AI solution continues to align with it.

The good news is that partnering with a reliable software development company like Techstack can take this burden off your shoulders and help make your ethical strategy work.

Conclusion

Developing systems with ethical AI in mind is essential if you want to future-proof your software and comply with regulations on responsible data governance.

This means prioritizing moral considerations and fostering an ethical culture when designing, developing, and training AI solutions.

At Techstack, we’re ready to help you navigate the risks and leverage the massive potential of AI to build reliable, bias-free software. Contact us: we’d love to get a conversation started.