Artificial intelligence seems like it could change the world, right? It’s capable of writing the perfect party invitation, telling jokes, or anticipating what we want to see/say next. And while it can do all these things, it’s important to remember that artificial intelligence has certain limitations and should be used with caution, at least for now.
What do we mean? Well, let’s go back to how artificial intelligence technologies are created. Data scientists feed computers and other machines with the information they want them to mimic, meaning this is the main source of information that machines have. Therefore, if there is any inconsistency or bias within the data, the computer will repeat that.
If artificial intelligence is used for fun, such as writing a poem for a friend, there’s no issue. But when AI is used for decision making or is expected to draw conclusions on its own, bias, whether intentional or not, can severely impact the accuracy of the result.
To understand exactly how ethics and bias play such a crucial role in artificial intelligence, let’s first cover the basics of AI and then give some examples of how bias and ethics can compromise the integrity of artificial intelligence.
What is Artificial Intelligence?
The ability of machines to replicate and mimic human responses and reactions to situations is what we know as artificial intelligence. By training machines to think like humans, we can automate otherwise tedious or repetitive tasks and use machine learning to process large amounts of data.
Artificial intelligence has evolved considerably over time, but still has a long way to go to properly imitate human thinking. Even so, however, already existing advances have transformed the way in which we view machines and their potential. In our day-to-day lives, artificial intelligence manifests itself:
In maps and transportation: ever wondered how your maps app can provide the latest information on traffic jams, closed roads, or the best route to take via public transportation, walking, or bike? Well, thanks to artificial intelligence, your maps app can update in real-time and provide you with the best possible experience.
In facial recognition/identification: through collecting data about your facial structure and features, your phone is able to both recognize that there is a face in front of the screen and verify your identity.
In writing assistance: spell check isn’t the only assistance you get from writing–thanks to the incredibly high amount of data that machines have been fed, they’re able to suggest what you can write next.
Artificial intelligence is quite useful in a wide range of applications–that much is clear. But as with anything, there are concerns and limitations of which to be aware. Now that we’re clear about what artificial intelligence is, let’s dive right into ethics and bias in artificial intelligence.
Bias in Artificial Intelligence
A machine can’t have bias, right? After all, it doesn’t have experiences or memories from which to form said bias. Unfortunately, that’s not quite the case: machines can only learn from the data they have and if this data is biased, incomplete, or of poor quality, the output of the machine will reflect the same problems.
The following are the most common examples of artificial intelligence bias:
Algorithm bias: if the algorithm itself that determines the calculations of the machine are incorrect or faulty, the results will be as well.
Sample bias: if the dataset you select doesn’t accurately represent the situation, your results will reflect this error.
Example: you’re collecting salary information, but only record those of male employees.
Prejudice bias: similarly to sample bias, prejudice bias uses data that is influenced by societal biases and therefore incorporates this prejudice into what should be opinion-free data.
Example: you’re evaluating the gender distribution in certain occupations, but only count female teachers and male doctors, creating an inaccurate skew in your data.
Measurement bias: measurement bias occurs when data is incorrectly gathered, specifically on how it was measured or valued.
Example: if employees are surveyed about their feelings about their employer and promised a reward if enough employees answer, those who are motivated simply by the reward may not give thorough or accurate responses.
Exclusion bias: you can’t pick and choose the data you use in your analysis and if you (intentionally or by mistake) exclude data points, your results will be inaccurate.
Example: if you think the middle-of-the-road answers to a survey aren’t consequential and remove them, you’ll end up with data skewed to both ends of the spectrum and an inaccurate representation of how the respondents actually feel.
Selection bias: while it can be quite challenging to get a big enough sample or one that’s representative of the entire population, choosing only certain groups can make your data completely useless.
Example: you want to evaluate the universities that high school graduates choose to attend, but ignore those who choose to immediately enter the workforce or attend community college, therefore painting an inaccurate picture of your graduates’ choices.
There are quite a few more ways that bias can appear in artificial intelligence, but the aforementioned ones are the most common. Here’s what you need to remember: artificial intelligence learns from the data that it’s fed and if that data is problematic or inaccurate, the outputs of artificial intelligence will be as well. Here’s what you can do to prevent bias:
Lots of situations involving bias stem from small or limited datasets; do everything you can to collect as much data as possible from as many sources as you can, diversifying your dataset.
As you begin to feed your computer with data, run tests during the early stages of testing to check for biases and correct them.
Explore online fairness and bias tests to make sure you caught everything.
Run your results by other experts to get other opinions and continuously check the quality of your data as time passes.
Ethics in Artificial Intelligence
You’ve definitely heard someone tell you that AI will take your job one day. And while the vast majority of jobs are safe (and those that AI can take over will morph into a different role), there are serious ethical considerations to keep in mind when discussing artificial intelligence.
One thing is clear: the power of artificial intelligence is massive and we’ve only just begun to uncover what it can do. But the following considerations are absolutely crucial when it comes to maintaining proper ethics in the future of artificial intelligence:
Privacy: we’re feeding machines tons of data about people to help it react in a more human-like way, right? How do we ensure that the data we’re giving to the machine is both secure and private? Prioritizing data privacy throughout the entire artificial intelligence lifecycle is one of the world’s main concerns.
Human dependence: yes, artificial intelligence is capable of automating some tasks that humans were previously handling and it can also handle much more data than people can. But it’s absolutely essential that AI isn’t left to make decisions on its own, as it will never replace human responsibility and accountability.
Sustainability: advances in artificial intelligence and technology are supported, but as long as they don’t come at the expense of the environment and overall sustainability.
Accessibility: new developments should be accessible worldwide, not just in highly developed countries with easy access to technology.
To ensure that ethics in artificial intelligence is prioritized, many countries and global organizations have come together to come up with policies and regulations, such as the GDPR in the European Union. But achieving truly ethical technological advances in artificial intelligence will come from a commitment from every individual, company, and country across the world.
The power of artificial intelligence is truly unmatched–but it’s on us to properly use it for good. And skilled artificial intelligence professionals are sorely needed across the tech industry, so if you’re interested in entering this up-and-coming field, look no further: there’s lots of room for advancement in artificial intelligence.
