Articles Posted in Technology

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Artificial intelligence (AI) has revolutionized document review, case analysis, and legal strategy. In the last five years, “technology-assisted review” (TAR) and newer generative AI tools have moved from experimental pilots to mainstream practice in U.S. litigation. For law firms, corporate counsel, and litigation support teams, AI in eDiscovery promises cost savings and efficiency—but it also brings admissibility challenges and ethical duties. This article explains the benefits, the federal and state evidentiary rules you must consider, and best practices for deploying AI in legal case management.

  1. Benefits of AI in eDiscovery

Faster Document Review: Machine learning can quickly sort millions of documents, flagging those most likely to be responsive, privileged, or high-risk. Predictive coding drastically reduces attorney hours compared to manual review.

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Introduction: AI Security Is the New Frontier

Artificial intelligence systems are no longer experimental and are embedded in financial fraud detection, autonomous vehicles, medical diagnostics, and critical infrastructure. Yet, AI security has lagged behind adoption. Hackers now target machine learning models directly, exploiting weaknesses unfamiliar to traditional IT teams. This article explains the top AI attack methods—adversarial examples, model poisoning, and data exfiltration—and outlines your legal obligations for breach response.

Understanding the AI Attack Surface

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Artificial intelligence is no longer a back-office experiment. It powers customer service bots, risk scoring, supply-chain predictions, and more. However, it must be noted that AI vendors are not just typical SaaS providers. They train models on data, may rely on subcontractors, and sometimes operate in opaque ways. That’s why AI vendor contracts need extra safeguards. A standard SaaS agreement often doesn’t address critical issues like model retraining, data ownership, or liability for AI-generated outputs. This article explains the 10 clauses that protect your business and the negotiation tips to secure them.

  1. Data Rights and Ownership

What to Cover

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Why Deepfakes and AI-Generated Media Are a Business Issue?

Deepfakes—the use of advanced artificial intelligence to create realistic but fake videos, images, or audio—are no longer just an internet curiosity. In 2024 and 2025, corporate security teams, compliance officers, and general counsel have seen a surge in fraud attempts and reputational crises driven by AI-generated content. From executives’ voices cloned to authorize fraudulent wire transfers, to fake customer reviews undermining brand trust, synthetic media is now a mainstream threat. Businesses that fail to anticipate this risk face financial losses, regulatory exposure, and reputational damage.

Understanding Deepfakes, Synthetic Media, and Fraud Risks

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Artificial intelligence (AI) is transforming the workplace. From résumé screeners to video interview tools and performance monitoring software, automated decision-making promises speed and efficiency. But for employers, these tools carry serious legal risks. When algorithms affect who gets hired, promoted, or fired, employers remain responsible under federal, state, and local laws. Missteps can trigger discrimination lawsuits, regulatory enforcement, and reputational damage. In this article, we’ll break down the federal employment laws, state and local AI regulations, recent lawsuits and enforcement actions, and a compliance framework employers can use to stay ahead.

Why Is Automated Hiring/Firing Legally Risky?

AI systems can unintentionally replicate or amplify human bias. For example:

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What Is Shadow AI?

Artificial Intelligence (AI) has become a powerful tool for businesses of every size. From chatbots that streamline customer service to data-driven algorithms that optimize supply chains, AI adoption is skyrocketing. However, alongside official AI deployments another trend called “Shadow AI” is growing.

Shadow AI refers to the use of artificial intelligence tools and systems inside an organization without official approval, oversight, or governance. Much like “shadow IT” in past decades, where employees adopted unauthorized apps or devices, shadow AI creates hidden cybersecurity, privacy, and compliance risks for businesses. With the rise of easily accessible generative AI platforms like ChatGPT, Bard, Claude, and open-source models, employees are bringing these tools into daily workflows — often without realizing the potential consequences.

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Artificial Intelligence (AI) is everywhere and is powering chatbots, approving loans, diagnosing illnesses, and even making hiring recommendations. These systems promise efficiency and accuracy, but what happens when AI gets it wrong? From wrongful arrests due to faulty facial recognition, to biased hiring decisions, to misdiagnoses in healthcare, AI errors can have serious legal and financial consequences. The pressing question is who’s liable? In the U.S. and globally, the law is still catching up. Liability for AI errors can depend on factors such as: (1) the role of human oversight; (2) whether the harm was foreseeable; (3) the contractual relationships between parties; and (4) applicable statutes, case law, and regulatory guidance. This article explores product liability, professional liability, data protection laws, and contractual risk allocation to help businesses and professionals understand who’s responsible when AI makes a mistake.

Why AI Mistakes Are Different from Human Errors

AI is not a “person” under the law. It cannot be sued, fined, or jailed. So, the responsibility flows to humans and organizations but pinpointing which party is responsible can be complicated for obvious reasons.

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Artificial Intelligence (AI) is transforming industries from finance to hiring and healthcare to law enforcement. Algorithms now help decide who gets loans, jobs, parole, and medical treatment. But while AI promises efficiency and objectivity, it can also replicate or even amplify existing biases hidden in the data it’s trained on to utilize.

Bias in AI isn’t just a technical flaw since it’s a legal risk. Across the United States and globally, discrimination laws that were written long before machine learning now apply to AI-driven decision-making. Businesses deploying AI must ensure compliance with statutes like:

  • Title VII of the Civil Rights Act (employment discrimination)
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Artificial Intelligence (AI) is transforming industries anywhere from personalized marketing to predictive healthcare and automated decision-making. However, as always, with innovation come legal challenges and questions such as how to handle personal data ethically and legally in compliance with privacy regulations.

If your AI system processes, stores, or trains on personal data, you are subject to data protection laws such as the California Consumer Privacy Act (CCPA), its amendment California Privacy Rights Act (CPRA), and the European Union’s General Data Protection Regulation (GDPR). This article breaks down what businesses need to know about AI and data privacy compliance.

  1. Why AI Raises Unique Privacy Concerns
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Artificial Intelligence (AI) is no longer just a tech buzzword since it’s embedded in business operations, government processes, healthcare, finance, and even our daily communications. However, as AI adoption accelerates, so do the legal, regulatory, and compliance challenges for companies, developers, and professionals. AI laws are evolving faster than ever this year. Governments around the world are introducing new rules to address transparency, bias, privacy, and accountability in AI systems. For business owners, executives, and legal teams, staying ahead of these changes is no longer optional — it’s essential. This article outlines the most important AI legal trends for 2025, why they matter, and how your organization can prepare.

The EU AI Act Begins to Take Effect

The EU AI Act, approved in 2024, is the world’s first comprehensive AI regulation. It classifies AI systems into risk categories — minimal, limited, high, and unacceptable — with different compliance obligations for each.

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