Understanding Purposefully Biased Data

Bias appears in nearly every dataset available today, even custom-created datasets. The dataset is often biased because the collection methods are biased, the analysis methods are biased, and the data itself is biased. You often see articles online with titles like “8 Types of Bias in Data Analysis and How to Avoid Them,” which means that people recognize the existence of bias and want to mitigate it as much as possible. However, sometimes you find that the opposite is true: The people using the dataset purposely bias it in some manner. Here are some areas in which data becomes purposely biased:

• Political: Political maneuvering can become the source of data bias. Two groups with opposing opinions will use the same dataset and obtain two completely different outcomes that support their particular perspective. At issue are the records selected and the dataset features used to create an outcome. In other cases, a group will resort to techniques like using bogus respondents in polls (pewresearch.org).
• Medical: When medical groups advertise for people to participate in trials of medications, procedures, and other needs, the group they get often doesn’t represent the population as a whole, so the data is biased.
• Legal: The use of COMPAS software (Correctional Offender Management Profiling for Alternative Sanctions) to predict the potential for recidivism is another example of data and algorithm bias, as explained in “Injustice Ex Machina: Predictive Algorithms in Criminal Sentencing,” (uclalawreview.org).
• Hiring: The use of datasets and well-rounded algorithms supposedly reduces the risk of bias in hiring and promoting individuals within an organization. According to “All the Ways Hiring Algorithms Can Introduce Bias” (hbr.org), the opposite is too often true. The datasets become an amplification of biased hiring practices within an organization or within society as a whole.
• Other: Anytime a dataset and its associated algorithms become influenced by bias, the outcome is less than ideal. The term machine learning fairness presents the idea that the outcome of any analysis should correctly represent the actual conditions within. If the outcome of an analysis doesn’t match the result received afterward, the analysis is flawed and the data usually receives a lion’s share of the blame.

Dealing with Data-Source Corruption

Even if people don’t cherry-pick data or use data sources that fail to reflect the actual conditions in the world, as described in the previous section, data sources can become corrupt. For example, when seeing product reviews on a website, you can’t be certain that:

• Real people created reviews.
• Some people haven’t voted more than once.
• The person wasn’t simply having an exceptionally bad (or less likely, good) day.
• The person actually used the product and has no ulterior motive, for example, if they sell the product or are a competitor.
• The reviews reflect a fair segment of society.

In fact, the reviews are likely so biased and corrupt that believing them at all becomes nearly impossible. Unfortunately, data-source corruption comes from many other sources:

• A sensor might be bad, producing erroneous results.
• A virus attack might cause data errors.
• The database or other software contains a flaw.
• Humans enter the data incorrectly into the database.
• Acts of nature, such as lightning, cause momentary glitches in data collection.

You can rely on a number of approaches to deal with all sorts of data corruption. Storing data in the cloud tends to reduce problems associated with hardware, weather, or other issues that cause data loss. Ensuring that you have procedures and training in place, plus constant monitoring, can help reduce human errors. Active administrator participation and the use of firewalls can reduce other sorts of data-source corruption.

Handling Botnets

Botnets are coordinated groups of computers that focus on performing specific tasks, most of them nefarious. This short section focuses on botnets that feed a dataset erroneous data or take over accounts to modify the account information in certain ways. Whatever means is used, whatever the intent, botnets generally corrupt or bias data in ways that cause any kind of analysis to fail.

Botnets represent a significant concern in AI for several reasons, primarily due to their evolving capabilities and the scale and speed of attacks they can launch. Here are three key reasons that botnets are a particular concern:

• AI-enhanced botnets: With advances in AI and machine learning, botnets have become more sophisticated, capable of automating and rapidly expanding cyberattacks. AI can enable botnets to analyze network behavior, adapt attack patterns to bypass cyberdefenses, and execute attacks with increased efficiency and stealth. This adaptability makes AI-enhanced botnets formidable adversaries, capable of evading traditional detection mechanisms and launching potent and destructive attacks.
• Scale and magnitude of attacks: AI-powered botnets can harness the computational power of numerous compromised devices, including the internet of things (IoT), creating massive bot armies. With these vast networks, AI-powered botnets can launch coordinated attacks that overwhelm even the most robust systems.
• Corruption of data sources: These botnets can manipulate datasets, flood AI systems with false information, and undermine the integrity of digital platforms. Such attacks can cripple the trustworthiness of AI-driven analysis and decision-making.