Privacy is becoming increasingly valuable. As more individuals learn of the risks of data breaches, their own vulnerability to AI monitoring, and their rights to protect their privacy, technology needs must be more compliant than ever. The consequences of corporate espionage, the use of spyware, and the collection of private user information have spilled onto tech companies’ profit-making abilities.
A company that does not comply with data protection standards is in a precarious position. Besides the risks of fines, license loss, and reputational ruin, non-compliant corporations face plenty of significant threats. Loss of customer and investor trust alone can seriously damage a corporation’s bottom line or survival chances. Therefore, it is essential that startups looking to employ artificial technology in their operations prioritize compliance.
Understanding the risks of non-compliance
There are potential human casualties to non-compliance with AI regulatory standards. When Target looked to predictive technology to improve their marketing, their course of action raised an ethical issue. About a decade ago, Target used an algorithm to predict which of their customers were pregnant, based on the clothing section they were shopping in.
They then sent pregnancy clothes coupons to these women, one of which had not informed her father of the pregnancy. This violation led to the creation of regulatory standards such as the EU’s GDPR and California’s CCPA. These standards question and monitor how much information businesses are collecting about their customers, whether their customers have consented to the collection of this information, and how it is used.
For businesses, a balance must be struck between achieving profitability by using legally-acquired information insightfully and adhering to compliance requirements.
Industries vulnerable to data misuse
The medical industry faces considerable data protection challenges. In 1996, HIPAA (The Health Insurance Portability and Accountability Act) was passed to protect individuals from having their private medical data sold, shared, and used to optimize corporate efficiency. This act was the result of an increasing vulnerability of individuals with pre-existing conditions to inflations of their medical premiums.
The U.S. being one of the richest countries on earth, but one of the few developed economies not to offer universal healthcare, insurance companies had all the leverage in using private information to demand higher payments from some individuals using their coverage. This raised a moral issue that is still frequently gleamed over, as insurance companies argue that comprehensive data is needed for their software and algorithms to function properly.
Personally Identifiable Information (PII) is, however, protected under U.S. law; therefore, corporations can be held legally accountable for breaching federal AI compliance regulations.
The evolution of AI data collection systems
To meet compliance requirements, AI systems have had to evolve into the use of highly complex algorithms. These algorithms simultaneously ensure the collection of comprehensive, detailed information but compounds it in a way that prevents programmers and engineers from delineating personal data. Their complexity grows by the day to ensure they can offer both privacy and accurate analysis.
These models and algorithms are also increasingly being used in the finance sector, which has always been vulnerable to information warfare. In the finance sector, data theft can lead to direct financial gain, and the latter can be substantial enough for hackers to dedicate considerable efforts to outsmarting the system. Therefore, governments are ethically required to monitor any AI data breaches that could facilitate data theft.
How do businesses achieve compliance?
All mid-size to large companies will hire legal and compliance professionals to ensure they tackle the regulatory and contract risks that come with profitability and impact. In AI-based companies, these professionals will provide the CxO suite with the information to effectively weigh competitive needs and demands. The CxO suite is tasked with managing the threats to the privacy of a system’s users.
Generally, legal teams will need to work with developers on achieving compliance. The CxO suite will make the final decision on how to balance efficiency goals and perceived user needs against compliance requirements. These decisions are often reflective of a company’s ethos and regard for its users.
The solution to the 'Lost in Translation' problem
It can be tempting for businesses to regard compliance as a privacy issue, legal issue, or engineering issue. However, it is predominantly a business issue.
Non-compliance can put service users at risk of their own information being used against them.
It can also sink customer trust in your business. This can discourage investors and cause the downfall of a company before it achieves profitability. Most field experts will be able to communicate effectively in their own language; therefore, expecting a company’s developers to be on the same page as its lawyers is very optimistic. This is where the need arises for a compliance manager; a professional trained specifically to help businesses to play by regulatory rules.
This post was written by Angela from Reciprocity Labs. Angela is a data enthusiast who has always loved numbers and fell in love with machine learning while attending Baylor University for a B.S. in Statistics and enjoys long walks through DataFrames in R or Python.
Cover image of post: Photo by Christopher Burns on Unsplash.