Table of Contents
Dramatic improvements
Artificial Intelligence in Recruitment: What You Need to Know
Why does this happen? A report from Reuters shows
Three Best Practices for Mitigating AI Bias in Recruiting
1. Always Keep Humans Informed
2. Conduct regular audits of artificial intelligence models
3. Patronize an AI recruiting software provider and hate bias
Views on the future of artificial intelligence
Translator Introduction
Home Technology peripherals AI Artificial Intelligence in Recruitment? There will be bias! Three excellent practices help you get it done easily!

Artificial Intelligence in Recruitment? There will be bias! Three excellent practices help you get it done easily!

Apr 12, 2023 pm 10:22 PM
AI robot recruitment

Translator | Cui Hao

Reviewer | Sun Shujuan

Although artificial intelligence is developing rapidly, we have not Experience the full scope of artificial intelligence (AI) and its capabilities. After all, the scope of AI’s impact and development prospects still dominate research, and scientists are still keen to find new application cases from AI innovations.

Artificial Intelligence in Recruitment? There will be bias! Three excellent practices help you get it done easily!

So far, we have encountered the application of artificial intelligence in various situations. That’s because many of the companies we patronize have increased their use of AI technology. One example is Siri, the interactive personal assistant that enables Apple customers to get information across a variety of apps, dictate emails and perform tasks using iOS phones, smartwatches, computers and TVs.

Many brands are also leveraging chatbots to deliver impeccable customer experiences in a way that not only drives sales but also eliminates repetitive tasks, thereby increasing human employee engagement Spend.

While the application rate of artificial intelligence tools is rising, investment by its companies is also soaring. According to McKinsey’s State of Artificial Intelligence 2022 report, 52% of respondents identified 5% of their digital budgets More than % is used for artificial intelligence. In 2018, this proportion was 40%.

Dramatic improvements

In terms of human resources, artificial intelligence can help companies improve employees’ satisfaction with their current roles and assist employees to complete tasks quickly, thereby saving money. Time and money are two indispensable factors. Beyond this, businesses can improve recruiting by using AI-powered software to sift through thousands of applications and narrow down a handful of experienced candidates.

However, there have also been instances where the system favored one particular group or gender over others. This is a dangerous trend that, if left unchecked, can significantly damage a company's image and negate the benefits of the technology. To help you effectively deal with bias in AI processing, this article provides some guidance.

Artificial Intelligence in Recruitment: What You Need to Know

In the past, people looked for job postings in classified ads in newspapers and responded by handwritten letters. Nowadays, anyone can obtain job information through a large number of channels on the Internet.

Recruiting is a company priority. This explains the increasing use of staffing agencies. Using artificial intelligence not only simplifies this process but also expands the possibilities for domain automation. From Tidio Focus on the impact of artificial intelligence on recruitmentSurvey found that nearly 67% of HR professionals admitted that this innovation had a positive impact on the recruitment stage.

But how does "prejudice" come unexpectedly?

In 2014, e-commerce giant Amazon chose to incorporate artificial intelligence into its recruiting system. While this was largely seen as a step in the right direction, as Amazon is a staunch advocate of automation, when something happened, the effort came to naught. The hiring system favors male candidates over female candidates.

Why does this happen? A report from Reuters shows

"...Amazon's computer models were trained to vet applicants by looking at patterns in resumes submitted to the company over a 10-year period. Most people Coming from men, this reflects the male dominance of the entire tech industry."

Bias in AI can manifest itself in many ways, with certain genders, groups, religions, and other affiliations wait.

Three Best Practices for Mitigating AI Bias in Recruiting

While it may not be possible to completely eliminate bias in AI models, there are some strategies that can reduce bias The probability of an event occurring.

The following are three methods that must be mastered.

1. Always Keep Humans Informed

While there are growing concerns that smart tools will replace human workers, we should think of this as a partnership rather than an outright of takeover. Also, the notion that AI-led tools should operate without human supervision because they demonstrate considerable efficiency needs to be revised.

Interestingly, human-machine collaboration has proven to be more valuable. "Harvard Business Review"'sA study Found that 1,075 companies across 12 industries experienced improvements in speed, cost savings and profits.

Given this fact, companies should ensure that there is a human team in place to continuously monitor the software used in recruitment. They can reduce the risk of favoritism. Additionally, employees should be drawn from a diverse talent pool so that every group is represented, thereby reducing discrimination.

2. Conduct regular audits of artificial intelligence models

If the probabilistic results of artificial intelligence algorithms are not regularly checked, it may undermine the company’s anti-bias efforts A serious blow. By organizing regular checks on algorithms, businesses can identify issues that prevent models from delivering fair results. Incomplete or inaccurate data should be corrected immediately upon discovery.

3. Patronize an AI recruiting software provider and hate bias

Just like a car dealership, you’ll find a variety of recruiting software options. To make the right decision, make an effort to understand how the mechanisms set up by the vendor address various biases.

You should require systems to be tested under various circumstances and observe how they perform. Bias aside, check the software's scalability, price, and cost savings. A good supplier is one that meets all or most of the criteria.

Views on the future of artificial intelligence

The development of artificial intelligence is a good thing for all of us. From enabling cars to steer and park themselves (with a driver actively supervising) to finding and hiring qualified candidates in less time, AI is showing advantages in recruiting. However, we should manage AI technology purposefully to avoid the mistakes associated with human effort.

Translator Introduction

Cui Hao, 51CTO community editor and senior architect, has 18 years of software development and architecture experience and 10 years of distributed architecture experience.

Original title: ##​Three Best Practices for Tackling AI Bias in Recruitment, Author: Michael Akuchie

The above is the detailed content of Artificial Intelligence in Recruitment? There will be bias! Three excellent practices help you get it done easily!. For more information, please follow other related articles on the PHP Chinese website!

Statement of this Website
The content of this article is voluntarily contributed by netizens, and the copyright belongs to the original author. This site does not assume corresponding legal responsibility. If you find any content suspected of plagiarism or infringement, please contact admin@php.cn

Hot AI Tools

Undresser.AI Undress

Undresser.AI Undress

AI-powered app for creating realistic nude photos

AI Clothes Remover

AI Clothes Remover

Online AI tool for removing clothes from photos.

Undress AI Tool

Undress AI Tool

Undress images for free

Clothoff.io

Clothoff.io

AI clothes remover

Video Face Swap

Video Face Swap

Swap faces in any video effortlessly with our completely free AI face swap tool!

Hot Tools

Notepad++7.3.1

Notepad++7.3.1

Easy-to-use and free code editor

SublimeText3 Chinese version

SublimeText3 Chinese version

Chinese version, very easy to use

Zend Studio 13.0.1

Zend Studio 13.0.1

Powerful PHP integrated development environment

Dreamweaver CS6

Dreamweaver CS6

Visual web development tools

SublimeText3 Mac version

SublimeText3 Mac version

God-level code editing software (SublimeText3)

Bytedance Cutting launches SVIP super membership: 499 yuan for continuous annual subscription, providing a variety of AI functions Bytedance Cutting launches SVIP super membership: 499 yuan for continuous annual subscription, providing a variety of AI functions Jun 28, 2024 am 03:51 AM

This site reported on June 27 that Jianying is a video editing software developed by FaceMeng Technology, a subsidiary of ByteDance. It relies on the Douyin platform and basically produces short video content for users of the platform. It is compatible with iOS, Android, and Windows. , MacOS and other operating systems. Jianying officially announced the upgrade of its membership system and launched a new SVIP, which includes a variety of AI black technologies, such as intelligent translation, intelligent highlighting, intelligent packaging, digital human synthesis, etc. In terms of price, the monthly fee for clipping SVIP is 79 yuan, the annual fee is 599 yuan (note on this site: equivalent to 49.9 yuan per month), the continuous monthly subscription is 59 yuan per month, and the continuous annual subscription is 499 yuan per year (equivalent to 41.6 yuan per month) . In addition, the cut official also stated that in order to improve the user experience, those who have subscribed to the original VIP

Context-augmented AI coding assistant using Rag and Sem-Rag Context-augmented AI coding assistant using Rag and Sem-Rag Jun 10, 2024 am 11:08 AM

Improve developer productivity, efficiency, and accuracy by incorporating retrieval-enhanced generation and semantic memory into AI coding assistants. Translated from EnhancingAICodingAssistantswithContextUsingRAGandSEM-RAG, author JanakiramMSV. While basic AI programming assistants are naturally helpful, they often fail to provide the most relevant and correct code suggestions because they rely on a general understanding of the software language and the most common patterns of writing software. The code generated by these coding assistants is suitable for solving the problems they are responsible for solving, but often does not conform to the coding standards, conventions and styles of the individual teams. This often results in suggestions that need to be modified or refined in order for the code to be accepted into the application

Can fine-tuning really allow LLM to learn new things: introducing new knowledge may make the model produce more hallucinations Can fine-tuning really allow LLM to learn new things: introducing new knowledge may make the model produce more hallucinations Jun 11, 2024 pm 03:57 PM

Large Language Models (LLMs) are trained on huge text databases, where they acquire large amounts of real-world knowledge. This knowledge is embedded into their parameters and can then be used when needed. The knowledge of these models is "reified" at the end of training. At the end of pre-training, the model actually stops learning. Align or fine-tune the model to learn how to leverage this knowledge and respond more naturally to user questions. But sometimes model knowledge is not enough, and although the model can access external content through RAG, it is considered beneficial to adapt the model to new domains through fine-tuning. This fine-tuning is performed using input from human annotators or other LLM creations, where the model encounters additional real-world knowledge and integrates it

Seven Cool GenAI & LLM Technical Interview Questions Seven Cool GenAI & LLM Technical Interview Questions Jun 07, 2024 am 10:06 AM

To learn more about AIGC, please visit: 51CTOAI.x Community https://www.51cto.com/aigc/Translator|Jingyan Reviewer|Chonglou is different from the traditional question bank that can be seen everywhere on the Internet. These questions It requires thinking outside the box. Large Language Models (LLMs) are increasingly important in the fields of data science, generative artificial intelligence (GenAI), and artificial intelligence. These complex algorithms enhance human skills and drive efficiency and innovation in many industries, becoming the key for companies to remain competitive. LLM has a wide range of applications. It can be used in fields such as natural language processing, text generation, speech recognition and recommendation systems. By learning from large amounts of data, LLM is able to generate text

Five schools of machine learning you don't know about Five schools of machine learning you don't know about Jun 05, 2024 pm 08:51 PM

Machine learning is an important branch of artificial intelligence that gives computers the ability to learn from data and improve their capabilities without being explicitly programmed. Machine learning has a wide range of applications in various fields, from image recognition and natural language processing to recommendation systems and fraud detection, and it is changing the way we live. There are many different methods and theories in the field of machine learning, among which the five most influential methods are called the "Five Schools of Machine Learning". The five major schools are the symbolic school, the connectionist school, the evolutionary school, the Bayesian school and the analogy school. 1. Symbolism, also known as symbolism, emphasizes the use of symbols for logical reasoning and expression of knowledge. This school of thought believes that learning is a process of reverse deduction, through existing

To provide a new scientific and complex question answering benchmark and evaluation system for large models, UNSW, Argonne, University of Chicago and other institutions jointly launched the SciQAG framework To provide a new scientific and complex question answering benchmark and evaluation system for large models, UNSW, Argonne, University of Chicago and other institutions jointly launched the SciQAG framework Jul 25, 2024 am 06:42 AM

Editor |ScienceAI Question Answering (QA) data set plays a vital role in promoting natural language processing (NLP) research. High-quality QA data sets can not only be used to fine-tune models, but also effectively evaluate the capabilities of large language models (LLM), especially the ability to understand and reason about scientific knowledge. Although there are currently many scientific QA data sets covering medicine, chemistry, biology and other fields, these data sets still have some shortcomings. First, the data form is relatively simple, most of which are multiple-choice questions. They are easy to evaluate, but limit the model's answer selection range and cannot fully test the model's ability to answer scientific questions. In contrast, open-ended Q&A

SOTA performance, Xiamen multi-modal protein-ligand affinity prediction AI method, combines molecular surface information for the first time SOTA performance, Xiamen multi-modal protein-ligand affinity prediction AI method, combines molecular surface information for the first time Jul 17, 2024 pm 06:37 PM

Editor | KX In the field of drug research and development, accurately and effectively predicting the binding affinity of proteins and ligands is crucial for drug screening and optimization. However, current studies do not take into account the important role of molecular surface information in protein-ligand interactions. Based on this, researchers from Xiamen University proposed a novel multi-modal feature extraction (MFE) framework, which for the first time combines information on protein surface, 3D structure and sequence, and uses a cross-attention mechanism to compare different modalities. feature alignment. Experimental results demonstrate that this method achieves state-of-the-art performance in predicting protein-ligand binding affinities. Furthermore, ablation studies demonstrate the effectiveness and necessity of protein surface information and multimodal feature alignment within this framework. Related research begins with "S

SK Hynix will display new AI-related products on August 6: 12-layer HBM3E, 321-high NAND, etc. SK Hynix will display new AI-related products on August 6: 12-layer HBM3E, 321-high NAND, etc. Aug 01, 2024 pm 09:40 PM

According to news from this site on August 1, SK Hynix released a blog post today (August 1), announcing that it will attend the Global Semiconductor Memory Summit FMS2024 to be held in Santa Clara, California, USA from August 6 to 8, showcasing many new technologies. generation product. Introduction to the Future Memory and Storage Summit (FutureMemoryandStorage), formerly the Flash Memory Summit (FlashMemorySummit) mainly for NAND suppliers, in the context of increasing attention to artificial intelligence technology, this year was renamed the Future Memory and Storage Summit (FutureMemoryandStorage) to invite DRAM and storage vendors and many more players. New product SK hynix launched last year

See all articles