AI Ethics 101: Navigating the Moral Landscape of Machine Learning
In Brief
Learn how to build AI systems for your organization ethically, ensuring the safety and privacy of everyone involved while safeguarding your company’s reputation.
Artificial intelligence (AI) has changed the way we live and work. Technology is influencing every field, from marketing and technology to healthcare.
AI enthusiasts are scrambling to understand how technology can solve the most complex problems the world grapples with today using machine learning (ML) as its bedrock.
ML is the process of feeding data to a system to enable the system to perform tasks. Now, that might not sound like anything new, but what’s fascinating about ML is that a system can use the data it’s given to self-learn the task and even get better at performing the task without needing a human to give it instructions explicitly, which was the norm before AI’s explosion.
This is why we’re heading towards things like self-driving cars, which were inconceivable before. Powered by ML, such cars and ‘learn’ to become better ‘drivers’ over time.
But, a word of caution.
AI is quickly taking over tasks that directly affect human life. Naturally, questions are being asked:
- Is AI fair, or is it biased?
- Will AI breach our fundamental human rights?
Such discourses have become known as AI ethics–the practice of identifying and addressing how we use AI without contradicting human values.
In this blog, we will discuss and navigate how to have difficult and frank conversations about aligning AI and ML’s moral compass.
What Is AI Ethics?
Ethical AI closely examines how AI interacts with and affects human society. People involved in ethical AI discuss how to build an AI system fairly–specifically in how AI makes decisions from data in a way that minimizes any risks.
To drive home the point, let’s use the example of surgery.
An example of healthcare AI could be providers training a system to help doctors prioritize patients on a surgery waiting list. In this instance, AI ethicists would make sure the system uses appropriate metrics to determine priority (like severity of medical condition), not unethical factors (like prioritizing people from richer neighborhoods.)
Additionally, ethicists would make sure AI is fed on fair data. If AI is given biased data to learn from, it will only perpetuate hurtful stereotypes.
Overall, the core of ethical AI is to create systems that benefit society and minimize harm.
It’s important not to get swayed by technological advancements to the extent it can jeopardize certain members of society.
Why AI Ethics Matters
Ethical AI protects an individual from harm in the following ways.
Protecting Fundamental Rights
AI in businesses often work with sensitive data, like a person’s financial or biometric information.
If ethical safeguards aren’t implemented, these systems could breach their human rights. For example:
- Data could be misused
- Data could be sold to malicious entities
- People could be subject to unauthorized surveillance
In this regard, ethical AI’s role would be to ensure these systems operate transparently.
Preventing Disparate Impacts
As intelligent as ML is, learning from data filled with human biases can have disastrous consequences. It would be like amplifying racism, sexism, and the like. The outcomes could result in:
- Biased lending decisions
- Unfair hiring practices
- Flawed legal rulings
Ethical systems design comes in to uproot cognitive and unconscious bias.
Addressing Existential and Societal Risks
AI misuse in a way that causes existential crises is a real problem. A prime example is deepfakes.
Deepfakes are the name given to creating hyper-realistic fake media. A malicious actor could create a deepfake (lookalike) of a celebrity and make it say anything it wants–just think about how damaging that could be to the victim and society at large.
Deepfakes can result in:
- The spread of misinformation
- Identity theft
Deepfake-based identity fraud is on a sharp rise. (Image source.)
Such consequences can be catastrophic during global events like general elections.
Key Ethical Questions in AI Development
It’s good that we’re raising important questions surrounding AI’s use, but how do we implement AI ethics? There are several questions to consider.
Who Decides What’s Right?
Who decides what’s right and wrong? After all, unless someone is following a strict code of conduct (like those found in organized religion), morality remains subjective.
What is your right could be my wrong.
So, who decides? (and who decides who decides?)
Should it be:
- The organization as a whole?
- A dedicated steering group?
- The government?
- The developers?
- The Pope?
Generally speaking, the best way forward is a diverse steering group that perhaps holds opinions across different ends of the spectrum. The more diverse input we get, the greater the chances of making a sound choice because each group can make up for each other’s respective AI blind spots.
And, as subjective as morality can be, there is a large part of it that has 99.99% human consensus, so the moral quagmire isn’t necessarily going to be complex each and every time, but we’d need group decision-making.
How Do We Prevent Bias?
AI systems must be designed to avoid discrimination against individuals or groups. Biases in training data can lead to unfair outcomes, such as denying loans based on demographic factors. Ensuring fairness requires diverse datasets and rigorous testing to detect and correct biases.
Are We Being Transparent?
People need to understand how AI systems make decisions. A lack of transparency confuses and diminishes trust, especially in critical areas like healthcare or criminal justice. Explainable AI means people can understand the reasoning behind decisions.
Are We Protecting People’s Privacy?
As an offshoot of transparency, systems should clearly communicate how user data is collected, stored, and shared–given how privacy is a primary ethical concern in AI.
Who Is Accountable When Things Go Wrong?
There needs to be a chain of command to follow when things go wrong.
Developers, organizations, or regulatory bodies must establish accountability frameworks to manage risks and provide redress for errors.
To What Extent Does AI Reasoning Replace a Human’s?
The human factor should never be taken out of the AI equation. AI decisions without human oversight can be damaging.
Impact on Jobs
AI has the potential to automate tasks, which can displace workers in various industries.
Companies feel AI-related layoffs are inevitable. (Image source.)
Ethical AI includes strategies to address these disruptions, such as retraining programs or creating new job opportunities to mitigate economic effects.
Misinformation
As mentioned, AI technologies like deepfakes can spread false information and manipulate public opinion.
Misinformation is arguably AI’s biggest challenge. (Image source.)
Ethical frameworks must focus on detecting and preventing the misuse of AI to safeguard the integrity of information and democratic processes.
When AI Goes Wrong: Real-Life Case Studies
The aforementioned concerns are valid, given how AI has gone wrong in specific instances over the last several years.
Biased AI Recruitment
Amazon’s AI recruiting tool penalized resumes with terms like “women’s,” favoring male candidates due to patterns in historical hiring data.
Algorithmic Discrimination in Government
The Dutch childcare benefits scandal is a glaring example of algorithmic bias in government applications. An AI system flagged low-income families and those with dual nationality as potential fraudsters, leading to false accusations.
Data Manipulation for Political Gain
The Cambridge Analytica scandal revealed how AI-powered analytics can be misused in politics. By exploiting Facebook users’ data, the company influenced the 2016 U.S. presidential election, sparking debates about data privacy and the ethical boundaries of AI in shaping political outcomes.
Steps to Develop Ethical AI Systems
As you can see, AI can be just as destructive as it is a source of good. As a result, there’s a huge need to develop AI ethically.
Here’s how.
Building Ethical AI Principles
Every organization needs an ethical AI SOP that outlines how they plan to use AI responsibly. These should become mandatory to publish. Good AI ethics prioritizes human rights, privacy, and democratic values.
This SOP then acts as an organization’s North Star. A report last year recommended AI companies spend 30% of their funding on R&D in safety and ethics.
And it’s not just for-profit companies who need ethical AI. Even top UK universities are developing guiding ethical AI principles.
Conducting Ethical Risk Assessments
It’s not enough to simply have a policy in place. Companies need to audit their AI development and usage regularly to identify kinks like privacy violations and discriminatory outputs.
Essentially, it’s using good AI (like predictive analytics that can foresee potential risks) to outwit bad AI (whether malicious or innocuous.)
Implement Sound Ethical Principles
Bright Data sets itself apart in AI and data collection by prioritizing ethical practices. They work with organizations like the World Ethical Data Forum to address the challenges of responsible data use in the tech world.
Clear ethical guidelines are their approach, supporting transparency and accountability in how data is collected and handled.
Their commitment is further demonstrated through initiatives like their Trust Center, which sets standards for ethical web data collection while safeguarding customer and partner interests.
By focusing on clear user consent and complying with regulations like GDPR and CCPA, Bright Data shows how responsible practices can go hand in hand with innovation. Their dedication to ethical practices has made it a standout in the AI and data collection space, setting an example of how innovation and responsibility can go hand in hand.
Final Thoughts
The ethical development of AI is essential for navigating the moral challenges ML poses.
When we address ethical concerns like privacy, fairness, and societal impact, we can help AI systems align with human values and promote trust.
For organizations, integrating ethical AI principles into their development processes goes beyond a moral or legal obligation. It is a prerequisite to responsible innovation.
Disclaimer
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About The Author
Jeremy Moser is the co-founder and CEO of uSERP, a leading digital PR and SEO agency. With a keen focus on driving organic growth and increasing online visibility, Jeremy has built a reputation for delivering impactful results in the digital marketing space. In addition to running uSERP, Jeremy is also an entrepreneur in the SaaS industry, where he buys and builds companies like Wordable.io. His deep expertise in SEO and content marketing is reflected in his work as a writer for notable publications, including Entrepreneur and Search Engine Journal, where he shares expertise on scaling businesses, growth strategies, and digital marketing trends.
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Jeremy Moser is the co-founder and CEO of uSERP, a leading digital PR and SEO agency. With a keen focus on driving organic growth and increasing online visibility, Jeremy has built a reputation for delivering impactful results in the digital marketing space. In addition to running uSERP, Jeremy is also an entrepreneur in the SaaS industry, where he buys and builds companies like Wordable.io. His deep expertise in SEO and content marketing is reflected in his work as a writer for notable publications, including Entrepreneur and Search Engine Journal, where he shares expertise on scaling businesses, growth strategies, and digital marketing trends.
