AI code of ethics: What is it, and why your company needs one

An artificial intelligence (AI) code of ethics is a set of principles designed to guide businesses in the responsible use of AI.

These policies focus on fairness, transparency, and accountability to address bias, privacy, and security. However, despite an overwhelming recognition of the necessity for AI guidelines, only 6% of businesses currently have them in place.

Here's why your company needs to join them.

Ethical AI vs responsible AI

You may well have heard of ethical AI and responsible AI within the realms of AI governance. It’s a good idea to clarify the difference between the two to clear up any confusion you may have about them.

Ethical AI concerns the ‘what’ — the moral principles of fairness, transparency, and human rights that guide the development of AI. Essentially, it defines the values AI should uphold to avoid causing harm and guarantees that society can benefit from it.

In contrast, responsible AI focuses on the ‘how.’ In other words, how the ethical principles will be implemented. It ensures that AI systems are designed, tested, and deployed specifically with accountability, compliance, and risk management in mind. Additionally, it includes corporate governance frameworks, bias mitigation, and regulatory adherence.

The two concepts are interconnected because without ethical principles, responsible AI lacks direction; without responsible implementation, ethical AI will only ever remain theoretical.

Ultimately, they should work together to create AI systems that are morally sound and practically effective.

Why are AI ethics important?

Given that the purpose of AI technologies is to improve upon or ultimately replace human intelligence, it is critical for businesses to have robust AI ethics in place.

If they don’t, the danger is that the AI projects they build into their operational processes can be influenced by biased human judgment, inaccurate data, or even plagiarized from copyrighted material.

This can have harmful consequences, particularly for people in creative or artistic industries (such as authors, artists, photographers, screenwriters, graphic designers, and voice actors), as well as underrepresented or marginalized groups and individuals. It can result in embarrassing PR disasters for businesses. (Who could forget Amazon’s blunder with their now-defunct sexist AI tool?) It can also result in costly lawsuits.

As AI becomes more integrated into daily life, it will increasingly shape hiring decisions, diagnose health issues, determine whether people are approved for mortgages, and even assess their likely guilt or innocence over lawfulness.

For these reasons, AI ethics must ensure such technologies operate responsibly and are built around a strong ethical framework that encourages public trust, wide-scale adoption, and a reduced fear of misuse.

Foundational principles of AI ethics

AI ethics are guided by several key principles, many of which align with the 1979 Belmont Report.

When applied to artificial intelligence, these principles take the following forms to guarantee they are used responsibly, fairly, and to benefit society.

1. Respect for persons

This principle emphasizes autonomy, informed consent, and the protection of vulnerable groups.

When applied to AI systems, it should respect the rights of individuals, ensuring AI systems are trained responsibly (and not on copyrighted material) and making sure people understand how their data is being used.

2. Beneficence

Following the ‘do no harm’ approach highlighted in the Belmont Report, AI models should always maximize benefits while minimizing risks.

AI developers, therefore, must constantly assess potential harms, such as bias and discrimination, misinformation, or job displacement. Doing so will help them implement safeguards to shape AI into positively contributing to society.

3. Justice

Justice centers around fairly distributing AI's benefits and burdens across society as a whole and not disproportionately toward certain disadvantaged groups or those experiencing social inequalities.

4. Transparency

The AI decision-making processes should be clear and understandable for everyone. However, users and stakeholders, in particular, must be able to trust the decisions of AI systems by knowing how they are made.

5. Fairness and non-discrimination

For it to work fairly, AI systems must be designed to mitigate biases and promote equitable outcomes.

This may involve addressing biases in training data, algorithms, and decision-making processes to prevent discriminatory behavior based on race, gender, or socioeconomic status. 

6. Data protection

AI has a fundamental responsibility to safeguard user data and respect a person’s data privacy rights.

Businesses can best do this by complying with data protection laws and putting systems in place that not only guarantee data sources are secure, but also give users control over their personal information.

7. Human accountability

Even though an artificially intelligent system is geared towards replacing human thinking, it should remain under human control and with clear lines of accountability. 

Collectively, developers, businesses, and policymakers must take responsibility for the outcomes derived by AI and, in particular, put in place mechanisms that can intervene as required.

8. Environmental impact and sustainability

The ecological footprint resulting from the use of AI must be considered and addressed. AI models can be energy-intensive and, therefore, contribute significantly to carbon emissions. For this reason, sustainability, optimizing efficiency, and minimizing environmental harm should be on the agenda when developing ethical AI practices.

How to develop your own AI code of ethics 

Creating an AI code of ethics can be quite complex, and its degree of difficulty will depend on factors like your use of artificial intelligence, the size of your company, and the industry in which you operate.

However, here’s a structured step-by-step process on how to develop one.

Step 1: Define your core ethical principles

To kickstart the process, identify the foundational ethical values your organization wants to uphold.

Take inspiration from the principles outlined above and consider existing ethical frameworks like the Belmont Report, the EU AI Act, and UNESCO’s AI Ethics Guidelines.

Step 2: Onboard stakeholders

It is important to onboard key stakeholders, including developers, business leaders, policymakers, IT teams, and end-users, to create your AI code of ethics.

Each of them will have a different perspective on how best to safeguard the system, help to identify potential ethical risks, and make sure the code reflects the needs of both your organization and society.

Consider running public consultations or advisory boards to ascertain even more perceptible insights.

Step 3: Assess AI risks and challenges

AI systems are prone to ethical risks, not least from biases in data and security and legal vulnerabilities but also in terms of their potential impact on the environment and the consequences their results may have on society.

For this reason, you would be well advised to put in risk assessment frameworks to determine what ethical safeguards are needed.

Step 4: Establish best practice guidelines

The next step of the process is to develop concrete guidelines that highlight how your ethical principles will be applied in practice.

This should involve outlining your best practices for data collection, making sure your algorithms are fair, establishing human oversight, and applying user consent.

You will also need to make sure these guidelines fall in line with relevant legal and industry regulatory requirements.

Step 5: Set up governance structures

Successful AI governance is reliant upon ethical compliance, so it is essential to define who will oversee this function within your organization.

Appointing AI ethics officers or establishing an ethics review board is a good way of monitoring your adherence, dealing with ethical dilemmas, and regularly evaluating your AI projects.

Step 6: Train your employees

AI ethics can only really work if your employees, developers, and stakeholders are trained in how to use and apply them.

For this reason, you should set up regular training sessions to guarantee that ethical considerations always remain a priority that is being met throughout the AI lifecycle.

It would be handy for them to be able to access online resources that can help them stay on the right path with their ethical decision-making.

Step 7: Monitor and update

As AI continues to advance, so will ethical challenges evolve, which is why you should habitually monitor your AI systems.

Doing this will make you aware of ethical concerns, enable you to gather feedback, and accordingly update your code of ethics.

It is also worth performing regular audits and impact assessments to maintain the overall integrity and compliance of your ethics.

Six key ethical challenges in AI

Given the rapid pace at which it is developing, AI presents significant ethical challenges that must be addressed if businesses are to certify that it is being responsibly developed and deployed within their organization.

Here are some of the key concerns to be aware of.

1. Bias

We have touched on how AI systems can inherit biases from training data, which could potentially lead to unfair outcomes, such as racial or gender biases in hiring tools and facial recognition.

Companies may need to embrace various mitigation strategies, including diverse datasets, bias audits, and algorithmic fairness techniques, to counter this.

2. Explainability

Many AI models, particularly deep learning systems, operate in ways that can be difficult to interpret, and this sometimes creates a lack of transparency and explainability that people mistrust.

This is particularly true in fields like healthcare and finance, where it is imperative that the information produced by AI is accurate and fair, which is why businesses can use explainable AI techniques, such as interpretable models and post-hoc explanations, to address this issue.

3. Data privacy

Some people are concerned about privacy breaches and misuse, which is understandable given the vast amounts of data AI systems process.

While organizations must comply with government regulations or GDPR, putting in place strong encryption protocols and gaining clear user consent to protect confidential data information is a recommended way to protect themselves from this issue. 

4. Misinformation

We are now well and truly living in the AI age. But we are also, unfortunately,  enduring an era of ‘fake news’, as evidenced by the amount of false information that seems to be out in the public domain. 

To trust the accuracy of the information they are generating, many companies use AI-powered detection tools, digital watermarks, and stricter content verification policies to verify it as being authentic.

5. Job displacement

There is a fear that increased automation and reliance on AI-driven decision-making will disrupt industries and potentially lead to job losses.

To eradicate this, governments and businesses must support workforce reskilling, promote human-AI collaboration, and develop policies to mitigate economic inequalities.

6. Generative AI plagiarism, hallucinations, and generation of harmful content

Generative AI models can produce inaccurate or ‘hallucinated’ information, plagiarize copyrighted material, and generate harmful content.

Therefore, it is vital that businesses incorporate accurate fact-checking, unbiased content moderation, and greater legal clarity on intellectual property rights with the development of their ethical AI.

Summing up

Any company that uses AI should make sure they put an appropriate AI code of ethics in place. Given that AI technology is here to stay, and global usage guidelines are just that guidelines and not laws — it’s up to individual businesses to have robust AI ethics in place.

Need guidance on ethical AI? Contact RecordPoint to find out how we can help your business navigate with confidence.

FAQs

How can businesses balance the benefits and risks of AI products?

Businesses might find it advantageous to take an applied ethics approach to AI by assessing both the benefits and risks of AI products.

Some of the ways they can achieve this include implementing ethical standards and following the ethics principles of organizations like the European Commission.

What are some of the key ethical challenges in AI research?

AI researchers face multiple ethical challenges in their line of work. Some of the main ones include bias in data sets, lack of transparency in autonomous systems, and the ethical implications of AI-driven decisions.

To help them address these ethical questions, companies are advised to develop transparent corporate cultures and comply with the ethics of artificial intelligence frameworks.

How can companies apply AI responsibly while ensuring ethical AI use?

A strong approach to AI should combine applied AI with clear ethical principles.

Companies may benefit from using explainability tools to clarify AI decisions, assess AI risks, and align with the global ethics of AI standards.

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