Human evaluation serves as the gold standard for assessing the quality of Natural Language Generation (NLG) systems. Nevertheless, the evaluation guideline, as a pivotal element ensuring reliable and reproducible human assessment, has received limited attention.Our investigation revealed that only 29.84% of recent papers involving human evaluation at top conferences release their evaluation guidelines, with vulnerabilities identified in 77.09% of these guidelines. Unreliable evaluation guidelines can yield inaccurate assessment outcomes, potentially impeding the advancement of NLG in the right direction. To address these challenges, we take an initial step towards reliable evaluation guidelines and propose the first human evaluation guideline dataset by collecting annotations of guidelines extracted from existing papers as well as generated via Large Language Models (LLMs). We then introduce a taxonomy of eight vulnerabilities and formulate a principle for composing evaluation guidelines. Furthermore, a method for detecting guideline vulnerabilities has been explored using LLMs, and we offer a set of recommendations to enhance reliability in human evaluation. The annotated human evaluation guideline dataset and code for the vulnerability detection method are publicly available online.

Artificial intelligence can help remove unconscious bias when recruiting to fill tech positions. But can it be a double-edged sword for tech leaders when it comes to promoting diversity & inclusion? The use of artificial intelligence (AI) is growing rapidly, infiltrating areas of business which have traditionally required humans to undertake what are often low-level tasks. With this comes the potential for artificial intelligence to help improve diversity & inclusion, using algorithms to arrive at decisions based around objective facts or statistics rather than subjectivity or bias. One obvious area is in the recruitment space, where AI has the potential to help organisations hire the best candidates, regardless of their background, or to improve representation of particular groups.

Debates about the efficacy and ethics of using artificial intelligence (AI) --or not using it--when making decisions about hiring, promotions, and diversity, equity and inclusion (DE&I) have been going on for years. And while the AI Bill of Rights has recognized and taken steps to protect vulnerable populations from unfair practices, new emerging AI technologies have led to a turning point of increasing adoption in the business landscape. But how can organizations ensure they are reaping the benefits of using AI to dramatically reduce bias and drive DE&I, while avoiding any potential pitfalls? When developed and used with intention, AI-powered technologies can be transformational tools for increasing diversity within businesses. Let's take a look at how leading organizations can leverage cutting-edge AI technology to augment human expertise and help organizations achieve their DE&I initiatives.

Protecting Consumer Privacy: AI can protect customer data through its ability to monitor network behavior and flag anomalies 24/7. Additionally, AI can accelerate the process of data identification to improve customer data privacy. Eliminating Biases: Because both conscious and unconscious biases are programmed into the data that an AI application is built upon, AI applications can become biased themselves. Fortunately, AI can actively mitigate the underlying biases of the models and systems deployed. Eliminating Mundane Tasks: Self-service AI-based HR portals enable employees to do for themselves what used to involve HR staff.

At any given time, a job opening on LinkedIn receives over 250 applicants. Unilever gets around 1.8 million applications a year for measly 30K positions, and screening through a trove of data isn't child's play. It is where the company deploys AI and big data in HR to run a series of tests to trace behavioral traits and then a list of successful candidates is passed onto the human recruiters. Surprisingly, Unilever ends up hiring around 50% of those candidates. Artificial Intelligence (AI) has proven its mantle on countless occasions, making it a viable option in the recruitment process. However, both good and bad outcomes have been well-documented in the past.

Can your brand explain how its artificial intelligence (AI) applications work, and why they make the decisions they do? Brand trust is hard to win and easy to lose, and transparent and easily explainable AI applications are a great start towards building customers' trust and enhancing the efficiency and effectiveness of AI apps. This article looks at Explainable AI (XAI), and why it should be a part of your brand's AI strategy. Typical AI apps are often referred to as "black box" AI because whatever occurs within the application is relatively unknown to all but those data scientists, programmers and designers who created it. Individually, even those people may not be able to explain anything outside of their primary domain.

Every time you ask Alexa to turn on your lights or play a song, you're using AI. But AI is also put to work in more serious ways, like facial recognition software by law enforcement. Some critics say there's a troubling lack of diversity among those who create the programs, and that is causing serious harm for people of color. We're joined now by Angle Bush. ANGLE BUSH: Thank you for having me.

AI, the buzz word known as Artificial Intelligence, in practice can be explained as Augmented Intelligence; a tool that has been in development since 1950s to extend human capabilities to complete tasks no human or machine could accomplish on their own. The world has already shifted towards building an AI integrated future and it's our responsibility to ensure it's heading in the right direction. "People are overlooked for a variety of biased reasons and perceived flaws; mathematics cuts straight through them" (Moneyball, 2011) Despite its immense potential, some major barriers still exist for AI hindering its progress. Biased behavior uncovered in current AI models has made us question if AI is the right way forward. To avoid such unfavourable outcomes and consequences, it is imperative to regulate the implementation of AI by an ethical framework assuring the key attributes; Transparency, Accountability, Privacy and Lack of Bias.

As AI begins to play a much larger role in our daily lives, informing healthcare decisions, making recommendations, helping us resolve customer service issues, talking with us as companion bots, making financial decisions, driving autonomous cars, and helping employees make more informed, faster decisions, it becomes more important that ethics and morality are built into AI applications. AI applications are making decisions that affect people's privacy, health, finances, jobs, criminal justice, safety, and overall happiness. Ethical AI is no longer an afterthought -- it must be built into the fabric of AI from this point forward. This article will look at the ways that ethics and diversity are being built into AI and the importance of doing so. To ensure that AI is ethical, it must be transparent and explainable.

Hiring bias is a colossal issue at work, particularly in areas such as hiring and promotion. The typical norms of hiring employees are profoundly defective and without a doubt tragic. The ultimate goal to indulge AI in controlled hiring bias is to expand the scope of hiring to include diversity in attributes such as gender, sexual orientation, color, experience, privilege, education, etc. Recruiters and HR organizations are reconsidering how they recruit to construct a faster and more efficient way of hiring. In an ideal world, the choice to enlist an applicant would be founded exclusively on their capacity to carry out the responsibility well. The recruit would be drawn nearer in a target, down to business way, liberated from subjectivity and unconscious hiring bias.