Discover the importance of ethics and bias in artificial intelligence
It seems like AI could change the world, right? Well, you can write a party invitation perfectly, tell jokes or anticipate what we want to see or say. However, it is worth remembering that, although it can help us achieve these and many other things, it has certain limitations and should be used with caution, at least for the moment.
What do we mean by this? First, we have to understand how AI technologies are created, since the people in charge of extracting and interpreting data feed computers and other machines with the information they want them to imitate , that is, this data is the main source of machine information. Therefore, the computer will repeat any inconsistencies or biases contained in the data.
If AI is used for recreational purposes, such as writing a poem for a friend, this is not much of a problem. But when AI is used to make decisions or draw conclusions on its own, bias, whether intentional or not, can seriously affect the accuracy of the results.
To understand the enormous importance of ethics and bias in artificial intelligence, it is necessary, first of all, to explain the basic concepts of AI and provide some examples of how these factors influence its integrity.
What is artificial intelligence?
We know as custom software development services such as artificial intelligence the ability of machines to replicate and imitate human responses and reactions to situations . By training machines to think like a person, we can automate tedious or repetitive tasks and use machine learning to process large amounts of data.
Artificial intelligence has made giant strides over time, but it still has a long way to go to adequately imitate human thinking. Even so, existing advances have transformed our conception of machines and their potential. Artificial intelligence is present in many aspects of our daily lives.
- On maps and transport : have you ever wondered where your mobile gets the latest information on traffic conditions, road closures or the best route to get somewhere by public transport, on foot or by bike? Well, it’s all thanks to artificial intelligence, which updates information in real time to offer you the best possible experience.
- In facial recognition or identification : By collecting data on facial structure and features, the phone can recognize that there is a face in front of the screen, in addition to verifying the user’s identity.
- In writing texts : in addition to the classic spell check, thanks to the enormous amount of data with which machines are trained, they can suggest what other things you can write.
It is evident that artificial intelligence is quite useful in many areas . However, as often happens, it implies certain limitations and problems that we should not overlook. Now that we are clear about what artificial intelligence is, let’s discuss the concepts of ethics and bias in artificial intelligence.
Bias in artificial intelligence
It stands to reason that a machine cannot have biases, right? After all, you don’t have experiences or memories to form preconceived ideas. However, this is not entirely true, as machines can only learn from the data they are fed, so if it is biased, incomplete or of poor quality, the machine’s responses will reflect the same problems.
Here are some of the most common AI biases:
- Algorithmic bias – If the algorithm that determines the machine’s calculations is incorrect or flawed, the results will also be flawed.
- Sampling bias : If the data set you select does not accurately represent the situation, the results will reflect this error.
- For example: You collect salary information, but you only record that of male employees.
- Prejudice bias : It is similar to sampling bias because this data is influenced by social biases and therefore incorporates them into results that should be unbiased.
- For example, you evaluate the gender distribution in certain jobs, but you only consider women for the teaching profession and men for the medical profession, which creates an inaccurate bias in the data.
- Measurement bias : Occurs when data is collected incorrectly, specifically in terms of its evaluation or measurement.
- For example, if you survey employees about their opinion of the company and promise them a reward if a significant number respond, people motivated solely by compensation may not give accurate or comprehensive answers.
- Exclusion bias : You cannot select which data to use in an analysis; If any information is excluded, intentionally or by mistake, the results will be inaccurate.
- For example, if you consider middle-of-the-road responses in a survey to be unimportant and eliminate them, you will end up with data that is skewed toward both ends of the spectrum and an inaccurate representation of the true opinion of the people surveyed.
- Selection bias : Obtaining a sample of the necessary size or that is representative of the entire population is very difficult, but choosing only certain groups generates worthless data.
- For example: you want to evaluate which universities people who finish high school want to go to, but you ignore those who decide to start working directly or those who opt for vocational training.
Bias in artificial intelligence can manifest itself in many other ways, but the ones we have mentioned are the most common. You must remember that artificial intelligence learns from the data we provide and that, if that data is questionable or inaccurate, its results will be too. Here’s what you can do to avoid bias:
- Many biased situations arise from small or limited data sets; Do your best to collect as much data as possible and from as many different sources as you can to diversify the data set.
- When you start feeding data to the computer, run tests during the early stages to check for biases and correct them.
- Conduct online fairness and bias tests to ensure you don’t miss anything.
- Consult the results with other experts for additional opinions and continually run checks over time.
Ethics in artificial intelligence
You have no doubt heard that AI will occupy our jobs in the future. The truth is that the vast majority of jobs are safe and those that AI can take over will be transformed into a different function; Even so, there are important ethical considerations that we must take into account when talking about artificial intelligence.
If one thing is clear, it is that the power of artificial intelligence is gigantic and we still do not know the scope it can have. In any case, it is absolutely essential to take into account the following aspects to guarantee an ethical future of AI.
- Privacy : providing machines with large amounts of data about people so they learn to react in a more human way. How can we ensure that the data we provide them is secure and private? Prioritizing data privacy throughout the entire lifecycle of artificial intelligence is a major concern globally.
- Human dependency : Yes, artificial intelligence can automate some tasks that people used to perform and also manage a lot more data; Now, we should not let AI make decisions on its own, as it lacks human responsibility.
- Sustainability : Advances in artificial intelligence and software development services are widely supported, but as long as they do not come at the expense of the environment and global sustainability.
- Accessibility – New advances must be accessible worldwide, not just in highly industrialized countries with easier access to technology.
To ensure that ethics in artificial intelligence is a priority, many countries and organizations globally have come together to develop policies and regulations, such as the EU General Data Protection Regulation. In any case, truly ethical technological advances in artificial intelligence will be the result of the commitment of all people, companies and countries in the world.
The reach of artificial intelligence is truly incalculable, but using it for good depends on people . Furthermore, people qualified in artificial intelligence are in high demand throughout the technology industry, so if you are interested in entering this very promising field, don’t think about it: AI is not going to stop growing.
