Workforce Technology
December 14, 2023

LinkedIn Prevails in Data Scraping Suit, Highlighting the Importance of Data Ethics in Staffing

Recent successes by platforms like LinkedIn against fake accounts provide a glimmer of hope in the ongoing battle for data integrity and ethical practices. However, these victories present only a partial view of the intricate legal and regulatory landscape that staffing agencies confront daily. In an environment where data holds currency and trust serves as capital, keeping abreast of legal developments is not merely a luxury but a necessity for survival and success.

Understanding the Challenges

Legal complexities aside, there are multifaceted challenges at play when considering data integrity in a digital era. But we’re not just talking about hackers and illicit data miners; AI is a new contender in the realm of data ethics.

  • The Rise of Fake Accounts: Fabricated profiles and manipulative bots distort search results, mislead employers, and erode trust in the entire ecosystem.
  • Data Privacy Concerns: Balancing the collection of valuable candidate information with respect for individual privacy rights becomes increasingly delicate amid stringent data protection regulations like GDPR and CCPA.
  • Discrimination and Bias: Unconscious bias and discriminatory practices within recruitment algorithms and selection processes can lead to legal repercussions and ethical dilemmas.
  • Misclassification of Workers: Navigating the murky waters of employee vs. independent contractor classification poses risks of miscalculated taxes, employee benefits disputes, and potential legal battles.
  • Evolving Regulations: The legal landscape for staffing is in a constant state of flux, with new laws and amendments emerging rapidly. Staying abreast of these changes is critical for avoiding compliance issues and legal pitfalls.
  • Imperfect AI: Pushing the popularity of large language models aside, they continue to hallucinate, offer erroneous information, sometimes steal or are given copyrighted content (another form of data scraping, especially when the content owner’s consent is absent), and remain questionable in the safeguards their developers use to prevent biases or ulterior motives in training. They are effective tools for expediting research, but they have a long way to go before they replicate the talent and skills of human users. A company’s overreliance on AI poses several risks. 

LinkedIn’s Legal Victory

This November, as Staffing Industry Analysts (SIA) reported, LinkedIn prevailed in its six-year lawsuit against hiQ, a now defunct company that scraped LinkedIn’s data. 

“The court ruled that LinkedIn’s user agreement unambiguously prohibits scraping and the unauthorized use of scraped data as well as fake accounts, affirming LinkedIn’s legal positions against hiQ for the past six years,” Sara Wight, VP, legal-litigation, competition and enforcement at LinkedIn, wrote in a post. “The court also found that hiQ knew for years that its actions violated our user agreement, and that LinkedIn is entitled to move forward with its claim that hiQ violated the Computer Fraud and Abuse Act.”

As SIA explained, “In its operations, hiQ attempted to reverse engineer LinkedIn’s systems to avoid detection by simulating human site-access behaviors, according to court documents. HiQ also hired independent contractors known as ‘turkers’ to conduct quality assurance while logged in to LinkedIn by viewing and confirming hiQ customers’ employees’ identities manually. When LinkedIn’s defenses restricted the turkers’ real accounts, hiQ instructed them to create fake ones.”

LinkedIn’s triumph over a massive fake account scheme sends a powerful message about the company’s dedication to data integrity and its commitment to holding bad actors accountable. These victories instill hope, demonstrating that industry leaders recognize the importance of clean data and ethical practices.

Staying Informed, Staying Compliant

Navigating this legal minefield and achieving success demands proactive efforts toward legal awareness and ethical conduct. At the most basic level, these are the steps that staffing companies, workforce solutions firms, MSPs, and related businesses should take to safeguard their operations, their employees, their candidates, and their customers.

  • Invest in Compliance Training: Ensure that your staff, from recruiters to compliance officers, receives training on relevant laws, regulations, and ethical best practices. Regular training sessions and accessible resources are vital for fostering a culture of compliance.
  • Implement Strict Data Security Measures: Prioritize data security through secure server systems, robust password protocols, and well-defined data breach response plans to safeguard candidate information and uphold trust.
  • Build a Diverse and Inclusive Culture: Foster a culture valuing diversity and inclusivity throughout the recruitment process. Implement unbiased hiring practices and leverage tools to counteract unconscious bias in AI algorithms.
  • Partner with Legal Counsel: Regularly consult legal professionals to stay informed about changes in the law and gain expert guidance on addressing specific legal challenges.
  • Embrace Transparency and Trust: Be transparent about your data collection practices, provide clear explanations on how candidate information is used, and respect individuals' privacy rights. Transparency builds trust and enhances your brand reputation.

Ensuring Ethical Data Use

Our clients and our talent have placed their trust in our abilities to keep their information secure, accurate and free from misuse. Here are some simple ways to ensure that technology leaders in the contingent workforce industry excel.

  • Develop or refine onboarding processes to include training that covers the ethics of data use and handling.
  • Bring in internal or external legal experts to coach team members on the legal obligations and best practices for data processing, storage, analysis and distribution.
  • Ensure that all applicable contracts or agreements contain solid terms and conditions for data standards, and that related stakeholders are knowledgeable of them.
  • Work to promote a business culture for tech teams that encourages open, supportive communications; team members need to be comfortable discussing or identifying topics related to data ethics, and managers must be willing to engage in those dialogs by creating a safe, repercussion-free environment.

No two companies will necessarily have the same processes, yet establishing and enforcing ethics standards is critical. They must be transparent, agreed upon, communicated, and monitored.

Artificial Intelligence (AI) and Large Language Models (LLMs)

With AI, and the newness of LLMs, the problem becomes trickier. Business leaders should turn to experts in data ethics for consulting before launching AI platforms as enterprise-wide solutions. Organizations like KPMG, for example, have created rudimentary standards. Consider KPMG’s “10 Ethical Pillars of Trusted AI.”

  • Fairness: AI solutions should be designed to reduce or eliminate bias against individuals, communities, and groups.
  • Transparency: AI solutions should include responsible disclosure to provide stakeholders with a clear understanding of what is happening in each solution across the AI lifecycle.
  • Explainability: AI solutions should be developed and delivered in a way that answers the questions of how and why a conclusion was drawn from the solution.
  • Accountability; Human oversight and responsibility should be embedded across the AI lifecycle to manage risk and comply with applicable laws and regulations.
  • Data Integrity; Data used in AI solutions should be acquired in compliance with applicable laws and regulations and assessed for accuracy, completeness, appropriateness, and quality to drive trusted decisions.
  • Reliability: AI solutions should consistently operate in accordance with their intended purpose and scope and at the desired level of precision.
  • Security: Robust and resilient practices should be implemented to safeguard AI solutions against bad actors, misinformation, or adverse events.
  • Safety: AI solutions should be designed and implemented to safeguard against harm to people, businesses, and property.
  • Privacy: AI solutions should be designed to comply with applicable privacy and data protection laws and regulations.
  • Sustainability: AI solutions should be designed to be energy efficient, reduce carbon emissions, and support a cleaner environment.

These considerations should be top of mind for businesses that are researching the AI, LLM, or chatbots to deploy across their organizations or teams. Ask the manufacturer questions, read the fine print. If the developer seems cagey or secretive, perhaps it’s time to look at a competing product.

Data Integrity Isn’t Just a Compliance Issue, It’s Good Business

Maintaining data integrity and ethical practices is not only a legal imperative but also a sound business strategy. Building trust with candidates and employers leads to improved talent acquisition, stronger partnerships, and ultimately, sustainable growth. By actively addressing legal and ethical challenges, staffing agencies can navigate the minefield of regulations and emerge as leaders in a trustworthy and responsible industry.

The journey toward a more ethical and legally compliant staffing landscape is continuous. By committing to ongoing learning, embracing transparency, and prioritizing ethical conduct, staffing agencies can navigate the complexities of the legal and regulatory landscape, build strong relationships with stakeholders, and secure their position as trusted partners in the talent acquisition process.

Photo by FLY:D on Unsplash

Continue reading

Our newsletter

Get great curated articles every week.

Combine sections from Ollie's vast component library and create beautiful, detailed pages.
No spam!

Innovative talent powering a brighter future.