Ethical AI is becoming a hot topic for governments and industry regulators, researchers, computer scientists, and organizations. As companies continue to embrace more advanced AI models for all use cases, new risks are emerging.
In recent years, we’ve seen major companies being bombarded with lawsuits, investigated by regulators, and facing backlash from customers for the misuse of AI systems. As a result, virtually every major technology company, from Microsoft and Google to Amazon and IBM, is rethinking how they design, implement, and fine-tune artificial intelligence.
So, what does it mean to create ethical AI systems? Why is it important, and what are the most significant challenges with AI and ethics that companies must overcome?
What is Ethical AI?
Ethical AI refers to developing and deploying AI systems that focus on fundamental values like fairness, transparency, accountability, and privacy. It’s a method of leveraging AI that aims to combat common issues with individual rights, manipulation, discrimination, and bias.
Governments and regulators are still working on defining ethical AI on a broad scale. After all, ethical AI isn’t exactly the same as “AI governance.” While governments can set legal limits on how companies use AI, deploying ethical AI would require every organization to ensure their systems address various ethical values, from environmental sustainability to trust.
For the most part, groups investing in ethical AI focus on the following:
- Principles: Specific guidelines and values that inform and govern the design, development, and deployment of AI. For instance, ensuring systems are designed with transparency, explainability, and a focus on minimizing technology misuse.
- Processes: The incorporation of principles in the design of AI systems that address both technical and non-technical risks. This might include keeping human beings in the loop when designing AI solutions or altering how AI algorithms work.
- Consciousness: Training employees and users on the ethical deployment of AI, and regularly reviewing systems for signs of poor ethical processes, such as bias, discrimination, or AI hallucinations (in the case of generative AI).
Why is Ethical AI Important?
Like most advanced technologies, AI has the potential to deliver exceptional benefits to the human race, but it can also harm our world. We’ve already seen plenty of examples of AI leading to biased hiring practices, plagiarism in the content creation world, and the replacement of human workers. Plus, criminals and malicious actors can use AI, too.
Bad actors are already using AI to create deepfakes for fraud, produce advanced phishing schemes, and hack various technology systems. As AI adoption continues to grow, and solutions like generative AI accelerate towards a potential value of $1.3 trillion by 2032, new risks are emerging.
The more data AI systems consume, and the more advanced the algorithms used to power AI becomes, the more novel risks emerge. For instance, in healthcare, an unethical AI system could prioritize certain patients based on their income when distributing health insurance. In law enforcement, biased AI systems could lead to false accusations and the imprisonment of innocent people.
Implementing ethical AI standards is how we ensure, as a society, we can safeguard against the various harmful outcomes AI adoption could introduce. Additionally, integrating AI and ethics will be essential for organizations to comply with evolving standards.
As AI adoption has become more widespread, governments are already implementing legislation that will influence how companies and individuals can use AI legally. In the United States, we have the Executive Order of AI safety. In the European Union, the AI Act will introduce a new set of GDPR-style rules for AI use, with hefty fines for noncompliance.
Ethics and AI: The Biggest Ethical AI Challenges
The challenges and issues organizations, government groups, and regulators need to address to implement ethical AI standards are constantly evolving. That’s particularly true now that we have more advanced and versatile AI systems than ever.
On a broad scale, some of the most significant challenges include:
1. Job Displacement
Ethical AI proponents have discussed the risks of artificial intelligence replacing human beings in the workplace for years. Goldman Sachs released a worrying report indicating that AI could eliminate 300 million jobs in the future.
Since then, the introduction of new AI systems, like generative AI models, has created further concerns for creative professionals and customer service experts. Notably, AI enthusiasts, as well as many researchers, have argued that every technology disruption has led to changes in the workplace.
For instance, in the automotive industry, many manufacturers have switched their focus to creating electric cars to protect the planet. This doesn’t mean the energy industry will disappear, but we’ll focus on a different type of energy economy in the years ahead. Similarly, AI might eliminate the need for certain jobs but create opportunities in other areas.
We might spend less time on mundane and repetitive tasks with AI to support us, but human beings will still be necessary for various roles – at least for now.
2. Ethical AI and Data Privacy
While there are many different AI systems intended for different use cases, all of the forms of artificial intelligence we have today rely on data. Many of the most significant AI models, such as LLMs, and generative AI solutions require huge volumes of data.
Ultimately, this means AI presents data privacy risks. Currently, very few companies share a lot of information about how the data used to train their systems are collected, processed, and stored. Clearly, the protection of data is crucial. Many regulators and policymakers have introduced new mandates over the years, based on our growing use of data.
In 2016, GDPR was introduced to protect the personal data of people in the European Economic landscape. In the US, individual states have developed policies, like the California Consumer Privacy Act. Even certain industries, like healthcare and finance, have their own rules for data management. Similarly, ethical AI will require the development of new data management policies.
3. Bias and Discrimination
Instances of bias and discrimination across numerous intelligent systems have led to many new questions about AI and ethics. Ultimately, machines can only use the data given to respond to queries and complete tasks. If the datasets used to train AI models are incomplete or biased, then the output of those systems will also be biased.
Already, companies attempting to automate certain processes, like customer service and hiring, have struggled to overcome AI bias and discrimination issues. Amazon unintentionally prioritized potential job candidates based on gender when using AI to fill technical roles.
Outside of the hiring landscape, bias can affect how AI works in various landscapes. A discriminatory system could harm the functionality of facial recognition software, social media algorithms, healthcare delivery, and more. Companies are taking measures to address this issue, by training models with more varied datasets, but there’s still work to be done.
4. Explainability and Accountability
Explainability and accountability are two of the biggest concerns in AI and ethics, and they often go hand in hand. Ultimately, regulators and consumers are no longer comfortable with AI developers simply introducing new systems and watching them work. They want to understand how AI systems function, make decisions, and complete tasks.
Unfortunately, the complexity of AI algorithms can make it difficult to understand why certain tools reach specific conclusions. This makes it hard to govern ethical AI usage effectively and leads to problems with accountability.
If we can’t explain why certain AI tools push companies to make decisions that lead to negative outcomes, it’s difficult to determine who is responsible for them. Should developers be punished for algorithms that make mistakes or the companies that use those algorithms?
If we ever reach a point where “technological singularity” occurs, and artificial general intelligence can make decisions independently, how will those systems be punished for unethical actions?
5. Security and Criminal Activity in Ethical AI
Security is a top priority for any organization embracing new technologies, whether it’s a software system for communications, or a new marketing tool. To enable ethical AI usage worldwide, companies and developers will need to take a broad approach to ensuring the security and safety of these systems. Just like any technology, AI can be susceptible to malicious attacks.
Even OpenAI, one of the world’s top AI developers, faced significant repercussions from a data breach in 2023. It’s not just the risk of criminals gaining access to sensitive data that companies need to be aware of either. If criminals gain access to intelligent systems, they could cause autonomous vehicles to act dangerously, or use AI to cause physical harm to other people.
As we mentioned above, AI is just as available to malicious actors as it is to everyday people. Criminals are taking advantage of AI to create advanced phishing attacks, hacking systems, and even deep fakes that override security measures. Intelligent systems can now replicate everything from a human being’s face to their voice, making it much harder for businesses to implement secure authentication methods.
6. Misinformation and AI Hallucinations
Poor datasets and lax training methods don’t just lead to bias and discrimination in AI outputs. They can also lead to AI hallucinations. This is what happens when a large language model or advanced AI algorithm perceives patterns that don’t exist, leading to incorrect outputs.
Many generative AI solutions, such as ChatGPT, Google’s Gemini, or Microsoft Copilot, can confidently answer questions incorrectly. This leads to confusion and poor customer experiences. AI systems can also be used to spread misinformation on a massive scale.
Malicious actors may use artificial intelligence to generate false information and use it to cause severe reputational damage for people or companies or affect public opinion. Once false information is shared online and through social media, it can be challenging to combat.
7. The Exploitation of Intellectual Property
The rise of AI writers and content creation tools like ChatGPT and Jasper has led to serious concerns in the ethical AI landscape. While these systems might seem as though they create content out of thin air, they don’t. They’re actually drawing information from pre-existing documents and resources. In some cases, those resources are the intellectual property of other creators.
In the last couple of years, numerous lawsuits have been filed against companies like OpenAI by individuals who claim their work has been stolen for training purposes. Ethical AI proponents are concerned that using AI for content creation will damage human creativity on a large scale and endanger authors’ ability to make a living.
As AI systems become more advanced, creating not just articles and novels but also videos, scripts, and images, more creative professionals are facing new risks. This further exacerbates concerns about job displacement and what the future will look like for content production.
8. Sustainability in Ethical AI
Ethical AI isn’t just concerned with protecting wellbeing and human rights. Many experts and researchers in the AI world are concerned about the impact that artificial intelligence will have on our environment. While AI can certainly help us achieve sustainability goals by monitoring carbon emissions, automating tasks that reduce our carbon footprint, and more, it can also harm sustainability.
The carbon footprint of AI is quickly growing as companies develop more advanced models. Training an AI system, like an LLM, requires significant energy. According to MIT, training a single AI model can emit more than 626,000 pounds of carbon dioxide – almost five times the lifetime emissions of five cars.
Ethical AI experts must find ways to train and fine-tune models that don’t consume as many crucial resources. Alternatively, significant investments in renewable energy models will be necessary to reduce the impact on our planet.
9. Loss of Social Connection
Finally, one major concern among some researchers and groups in the AI landscape is that a growing dependence on AI will harm human connections. In the customer service landscape, for instance, AI has the potential to deliver highly personalized experiences to customers. It can customize search content based on your needs and suggest products based on previous purchases.
Powerful AI bots can even help customers troubleshoot issues and address common concerns. However, AI can’t show empathy and can’t form genuine connections with human beings. AI systems are being developed to replace all kinds of human connections, from AI tutors who deliver educational resources to students to AI therapists.
While these solutions could be beneficial, allowing more people to access customer service, therapeutic support, and mentoring services, they could also lead to fewer human connections over time.
The Evolution of Ethical AI: Stakeholders in AI and Ethics
Although AI has existed for some time, ethical AI is a newer concept. In the past, AI and ethics weren’t a priority for most innovators. Developers and business leaders were primarily concerned with accessing the most powerful, advanced tools possible.
Now, however, virtually everyone is recognizing the importance of ethical AI. While governance guidelines and regulations are still being developed, various stakeholders are getting involved – all playing a role in shaping the future of artificial intelligence. Some key stakeholders include:
- Academics: Professors, researchers, and scientists are investing in ideas that will support governments, corporations, and other organizations in developing safe and ethical AI systems.
- Government: Government agencies and committees are introducing new legislation, acts, and mandates intended to govern the use of AI for specific regions. Even intergovernmental entities, like the United Nations and the World Bank are getting involved. For instance, UNESCO’s 193 member states signed the first global Ethics of AI agreement in 2021.
- Nonprofit organizations: Nonprofit organizations are emerging with a focus on addressing ethical AI concerns, such as job displacement, bias in AI systems, and misrepresentation. These groups are working with those affected by AI issues to overcome injustices.
- Private companies: Executives at AI-focused companies like Google, Microsoft, Meta, and beyond are all developing their own ethical AI guidelines. Most leading technology companies have introduced their own “responsible AI” frameworks and are training team members on the safe use of artificial intelligence.
How to Implement Ethical AI Practices
Creating ethical AI isn’t something one company or individual can do alone. It requires a close consideration of the ethical implications of this technology on a global scale. We still don’t have a global governance framework for AI and ethics at this stage.
This means there’s no one-size-fits-all approach to ensuring AI performs safely and is developed, trained, and used in a way that protects human beings. Going forward, enabling ethical AI practices will require a new approach to governance.
Companies and organizations will need to implement new strategies for overseeing AI’s lifecycle through policies, processes, systems, and staff. They’ll also need to educate all people involved in AI development on its safe usage and establish policies for AI optimization.
Core Focus Areas for AI and Ethics:
While there’s no single strategy for implementing ethical AI yet, there are specific areas that organizations will need to focus on, such as:
- Preventing harm: Risk assessments will need to be conducted regularly to prevent AI from causing harm or affecting the safety of others. This will require a comprehensive approach to governing AI usage and minimizing bias, discrimination, and misinformation.
- Privacy and security: Organizations must take a comprehensive approach to reducing their vulnerability to security risks and protecting personal data. Privacy must be promoted and protected throughout the entire AI lifecycle.
- Responsibility and accountability: AI systems will need to be auditable and traceable. Due diligence, oversight, and impact assessment mechanisms will need to be in place to avoid conflicts with human rights and well-being.
- Transparency and explainability: Ethical AI depends on the transparency and explainability of our systems. Organizations will need to ensure they can explain how their systems work.
- Awareness and literacy: It will be crucial to ensure the public understands the nature of AI, how it works, and how they can use it safely. Digital skills and AI ethics training will be critical in the years ahead to ensure ethical AI usage.
- Collaboration: International lawmakers, governments, and companies will need to work together to establish holistic strategies for the ethical use of AI. Governance standards will need to be established that impact all AI users.
The Future of Ethical AI
The development of Ethical AI is still in its early stages. We’re constantly developing newer, more advanced models for artificial intelligence. However, we still don’t have a strategy for fully governing AI and protecting its use.
Many experts believe that ethical AI will be essential to safeguard human well-being. We expose ourselves to numerous significant risks without an ethical approach to AI development, deployment, and optimization.
The good news is that steps are already being made to help ensure ethical AI usage in the years ahead. However, there’s a long way to go before we master AI ethics globally.
