Increasing diversity, equity, and inclusion (DEI) through technological solutions
A master’s thesis in cooperation with the Chair of Strategy and Organization at the Technical University Munich.
Supervisor: Dr. Theresa Treffers
Chair of Strategy & Organization: Prof. Dr. Isabell Welpe
Student: Britta Felzmann
- Increasing diversity, equity, and inclusion (DEI) through technological solutions
- ⭐ Key Findings
- ℹ️ About
- 🖼️ Infographic on the Topic
- 🌍 The DEI Tech Market
- 👩💻 Technologies for DEI Tech
- 🧩 Areas of DEI Tech
- 🤿 Deep Dives
- 📚 Further reads
- 💽 Full Database with respective categories
- 🗞️ Sources
⭐ Key Findings
📌 For most issues identified by the literature where individuals from underrepresented groups face adversity in the workplace exist tech tools that aim at solving them
📌 The area of employee assessment and performance management is a space with a lack of tech solutions
📌 Future research is needed to identify the long term effectiveness of bias interventions that are supported by Virtual Reality
📌 Trackers for diversity representation in societal fields like media and business should be extended to include more underrepresented groups
📌 DEI tech still very focused on the recruiting area, since inclusion and equity are gaining importance, this is expected to change in the future
ℹ️ About
The topic of diversity, equity, and inclusion is increasingly discussed in society and the workplace. Global movements like MeToo and Black Lives Matter, in particular, have driven this development and DEI also simply makes sense for companies. It is proven that diverse teams are more creative and can be more effective and performant than teams of people with the same background. However, to reap these benefits, inclusion and equitable practices are considered prerequisites.
Unfortunately, companies are not progressing as much in this area as hoped for. A 2020 McKinsey study found that 15% of leadership teams in 2019 were made up of women, while just 13% of leaders were from ethnic minorities in the U.S. and UK in the same year [1]. Furthermore, employees lack inclusive business environments [2].
To address the lack of diversity, equity, and inclusion, technologies have come up that bypass the negative impact of (un-)conscious biases and structural inequities. These technological tools - or in the following called DEI tech - are explored on this page.
🖼️ Infographic on the Topic
🌍 The DEI Tech Market
Over 60% of the DEI Tech market is located in the United States, followed by 13% in the UK. The remainder of the companies and tools is distributed over Canada, Australia, Singapore, Denmark, Norway, Germany, Switzerland, Sweden, the Netherlands, Poland, Iceland, and Greece.
This picture is also reflected in the worldwide funding amounts for DEI Tech. The total funding amounts add up to around 2.9 billion US Dollars, of which 77% flow into companies in the US. Nine percent of the funding is located in Australia, where 
Company Size
Companies operating in the DEI tech market are mostly classified as micro or small enterprises (70%). This indicates a market with many startups, which are also younger, as more than half of the companies were founded after 2016.
Market areas
The market areas where DEI Tech vendors operate fall into 6 categories (Click here for an in-depth explanation of these areas in a DEI context. The largest percentage of companies (37%) provide recruitment solutions, followed by 25% of firms operating in the employee engagement & retention space. The next most saturated area is attraction of employees, where 18% of companies offer a solution. Next up are mentoring & career development (10%), technology-supported DEI learning (8%), and employee assessment & performance management (1%).
👩💻 Technologies for DEI Tech
Scientific papers mention six technologies that are used to solve problems related to DEI. These technologies are Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), Virtual Reality (VR), Computer Vision, and Analytics. The sample collected for this thesis reflects these findings.
AI is the most used technology in Human Resources Management. Since Machine Learning is a subset of AI and is often connected to AI for technological applications within Human Resources Management, it is concluded within this section.
Within the employee lifecycle, Artificial Intelligence and Machine Learning are deployed in multiple areas within the DEI context:
- Machine Learning in combination with NLP is used to screen job advertisements or other textual content within companies for biases and exclusionary language. Exemplary companies are
Develop Diverse or
Textio. In this context, NLP structures the text so that Machine Learning algorithms can analyze for biased patterns.
- Both AI and Machine Learning can be used for the pre-screening of candidates. The technologies predict the likelihood of someone being successful in the job based on criteria that are pre-determined by the employing company or through analyzing successful teams within the company.
Pymetrics is an interesting example of the use of Machine Learning in pre-screening. The startup has developed a game-based approach where successful employees at the hiring company play a number of games that were validated by psychological studies for the purpose of recruiting. Based on the performance of these employees, a model is trained by a machine learning algorithm that automatically evaluates the performance of applicants within the games and predicts how successful they will be in the job.
HiredScore evaluates candidates not based on games but uses AI to screen them based on criteria that are chosen by the hiring company.
- Another use case is conversational AI, as used by
Allie, that conducts conversations with candidates automatically to determine their fit for a specific role.
Retorio deploys AI to evaluate applicants within video interviews. For this, it compares their facial expressions with the Big 5 personality traits to determine how well they fit into a company’s culture. See the further reads for more insights into this approach.
Pymetrics. The start-up has open-sourced its code and done an external audit to free its systems of any biases. Go to further readings for more information. Retorio is using the FairFace [3] dataset for their facial recognition which has been diligently developed to include most groups within the population. - NLP is used to detect spoken or written language biases for DEI purposes. NLP is often combined with machine learning technology to identify biased language in job advertisements or textual content published within a company (see the companies
- NLP can also be used to redact any demographic information from resumes while leaving sentence structures intact.
MeVitae is an example company.
- Another very specific use case of NLP is presented in the
Gender Gap Tracker. Here, NLP is utilized to determine gender representation in media content.
Using Virtual Reality to change attitudes towards a different group is becoming increasingly relevant in the literature on DEI technology.
- VR is used in practice to intensify the experience of interventions to reduce biases.
Equal Reality,
Vantage Point and
Praxis Labs do VR training on topics like unconscious bias, harassment, or microaggressions.
- One approach that is suggested by Zaleski (2016) [4], is that the applicant can disappear behind an avatar so that the recruiter cannot judge the person based on their appearance.
Unbent offers something similar. This startup creates a VR space where applicants can choose an avatar of their liking and their interaction with other people in a workplace setting is tested.
Computer vision is useful to automate the analysis of visual data. The 
Analytics tools are often referred to as People Analytics in an HR context. Organisations can support their decision-making through analytics based on an objective, data-driven approach. Analytics can determine pay gaps of diversity representation and create valuable insights for the development of DEI initiatives.
Within the data sample, it was found that the analytics solutions either target a specific problem area (mostly pay gaps, see 
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🧩 Areas of DEI Tech
Six areas have been identified that are related to the workplace, where the scientific literature lists problems that people from underrepresented groups are facing, or where traditional approaches to improving DEI are not sufficient.
Categories
Employee attraction is the first stage of the employee lifecycle where potential candidates become aware of a company as employer. There can be differences in how people see the external image of an employer.
For example, while men rate a high salary as more important when considering an employer, women are attracted more towards employee diversity, work-family balance, and similar colleagues. Similarly, underrepresented identities emphasise diversity within the workplace (Backhaus et al., 2002; Thomas & Wise, 1999) [5][6].
Another important aspect is the wording within job advertisements. For example, stereotypically male language in job ads can decrease the number of women that will apply for an occupation (Gaucher et al., 2011; Hentschel et al., 2020) [7][8].
An adjustment of how a recruiting message is conveyed can positively influence whether an applicant feels threatened in their identity by the advertisement – so whether a so-called Stereotype threat is triggered – and therefore if the applicant is attracted towards applying or not (Liu et al., 2021) [9].
Recruiting is the area where the tech space is most saturated and also where most issues for underrepresented identities are listed in the scientific literature.
In a study performed in the Netherlands, Derous and Ryan (2018) [10] found that people with Arab names received fewer callbacks than those with Dutch names. The same adverse effects have been found for African American names on applications compared to typically white-sounding names in the US (Bertrand & Mullainathan, 2004; Dovidio & Gaertner, 2000) [11][12].
Further, women’s qualifications are often rated lower than men’s, even if they are the same or strongly comparable (Campbell & Hahl, 2020) [13].
Within interviewing, mental processes and unconscious biases can impact the result of the interview negatively for underrepresented identities (Gaucher et al., 2011).
Factors outside of interpersonal biases that can keep diverse applicants from being recruited can also result from institutional or structural discrimination like the recruitment preference from prestigious universities (Bogen & Rieke, 2018) [14].
Literature has found significant differences in the way women and minority employees are generally rated in performance reviews and assessments of how well they are performing in their job. Performance reviews are a relevant factor in determining someone’s compensation and steering career trajectories.
In an earlier study, McKay and McDaniel found that white employees were rated significantly higher than their black counterparts within performance ratings (2006) [15].
A recent study examined language differences between performance reviews for men and women found that women are often described as more communal in their personality and communication style (Correll et al., 2020) [16]. They have less favourable future-oriented reviews. Furthermore, they are less likely to be related with ‘standout words’ like ‘visionary’ or ‘brilliance than men .
Racial minorities and women receive less mentoring on higher levels within a company. Therefore, they are appointed to corporate boards in smaller numbers than White men (McDonald & Westphal, 2013) [17].
Women and people of colour are also at a disadvantage in their assignments at work - receiving less prestigious tasks than white men (Williams & Multhaupt, 2018) [18].
A Canadian study also found that racial minority employees are less likely to be promoted than their White counterparts, particularly at lower organizational levels (Yap & Konrad, 2009) [19].
Engaging and retaining diverse employees is deeply connected to inclusion and how integrated they feel into the workplace (Brown, 2018) [20]. An essential way of fostering the feeling of inclusion is employee voice. Voice is understood as how employees can influence decision-making and other aspects like activities at work.
Voice mechanisms like anonymous feedback channels or dedicated networks have shown to be very important for underrepresented identities like members of the LGBTQIA+ community (Bell et al., 2011) [21].
Unconscious bias trainings are commonly used to improve DEI at the workplace. In 2017, companies in the US have spent 8 billion dollars on diversity training (Kirkland & Bohnet, 2017) [22].
However, except for initiatives that were scoped for an extended period (Rudman et al., 2001) [23], few positive results have been reported for these types of training (Forscher et al., 2019) [24]. On the contrary, it has been shown that these initiatives can even lead to increased discriminatory behavior (Kalev et al., 2006) [25].
🤿 Deep Dives
Problems identified in literature | Tech solution | Example companies | |
URIs are attracted by different characteristics of a job or company | Analytics for the attraction space in the hiring funnel ⇒ the insights can be used for appropriate branding | ||
Exclusionary wording | Tools that flag biased or exclusionary language and suggest different words | ||
Platforms that bring together job seekers from URGs and companies looking to increase their diversity representation | |||
Tools for active sourcing that suggest jobs to URIs | |||
Active matching solutions to suggest URIs to companies based on their answers on questionnaires or tests |
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Problems identified in literature | Tech solution | Example companies | |
People with names connected to being from an underrepresented group receive fewer callbacks on their resumes, also qualifications are rated differently based on demographic factors | - Tools for anonymizing and redacting resumes and profiles
- Pre-screening tools that automatically evaluate candidates before an interview
- Gamified testing of applicants’ skills
- Skill assessment in VR environment | ||
Implicit biases in interviews | Companies providing structured interview guidelines for comparability of interviews | ||
Structural discrimination | Sourcing tools suggest diversity-friendly filters or sources automatically while only taking skills into account | ||
Tools focused on technical recruiting to solve issues of unfairness within technical interviews and coding challenges | |||
Recruiting analytics that surface injustices within the recruiting process |
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Problems identified in literature | Tech solution | Example companies | |
Lower ratings of URIs | |||
Different wording in assessments of women | |||
Tools that offer pre-defined templates for objective performance reviews. | |||
Problems identified in literature | Tech solution | Example companies | |
URIs receiving less mentoring | DEI-focused matching platforms for mentees and mentors | ||
Less prestigious assignments for people from URGs | |||
URIs disadvantaged in promotions | Tools that match employees to job openings based on their skillset |
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Problems identified in literature | Tech solution | Example companies | |
Giving a voice to URIs | Tools with surveys that give insights on inclusion and potentially offer whistleblower hotlines, one-on-one feedback channels, or offer strategies directly from the survey results | ||
Some solutions provide different improvement options for the experience of URIs | - Language analysis within corporate context:  ishield
- Self-assessment for leaders with improvement suggestions:  Fortay
- Background checks for employees before they are employed for toxic comments in publicly available content:  Fama
- Virtual Meeting plugin that tracks speaking times:  Equalicert | ||
Analytics solutions for inclusion and equity within the company |
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Problems identified in literature | Tech solution | Example companies | |
VR-supported interventions | |||
Bias interventions without VR |
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Tech solution | Tools |
Diversity tracking in media content | |
Diversity tracking in business and tech |
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📚 Further reads
Pymetrics AI Audit: https://evijit.github.io/docs/pymetrics_audit_FAccT.pdf
Retorio Fairness approach:
GD-IQ: https://seejane.org/research-informs-empowers/data/
Gender Gap Tracker: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0245533
💽 Full Database with respective categories
Name | Area | Desciption of functionality in this area | Technology | Website | Country | # of Employees | Latest Funding Round | Total Funding Amount (in US $) | Source | Founded Year |
|---|---|---|---|---|---|---|---|---|---|---|
Testhub | Attraction | Employers can post jobs on a platform specifically for diverse talent. Applicants register and take a short test to be matched to potential employers. | Norway | 1-10 | 0 | Crunchbase | 2019 | |||
Jobseekrs | Attraction | A job-matching platform where jobseekers answer a short questionnaire to determine their culture fit. Companies looking to hire also answer questions on their company culture. An AI algorithm trained on unbiased data matches applicants and companies. After matching, companies can schedule an interview with the applicant through the platform. | United Kingdom | 1-10 | 0 | Crunchbase | 2017 | |||
Brilliant Hire by SAP | Attraction | Increases the ratio of visitors to applicants on a companys career page through AI-powered personalized job recommendations. Aims to increase diversity through encouraging diverse talent. | Artificial Intelligence, Analytics | United States | 1-10 | Seed | 1000000 | Crunchbase | 2018 | |
hackajob | Attraction | This is a job posting platform which specifically aims to bring diverse talent together with employers. The applicants sign up to the platform and post their skills, without any background information. Companies can then reach out to them. The pltform is targeted at tech talents. | Analytics | United Kingdom | 51-100 | Series A | 7900000 | Crunchbase | 2014 | |
PowerToFly | Attraction | This is a platform which brings diverse job seekers into contact with employers that are interested in hiring with diversity in mind through events. | United States | 51-100 | Series A | 7500000 | Crunchbase | 2014 | ||
ersity | Attraction | This tool is a job board specifically targeted to underrepresented candidates. | United States | 11-50 | 0 | Mattermark | 2017 | |||
Develop Diverse | Attraction | The tool analyzes job ads for language that is potentially not inclusive and flags which groups might feel excluded. The tool uses AI to keep up with continuous language evolution. | Artificial Intelligence | Denmark | 11-50 | Venture - Series Unknown | 2500000 | Crunchbase | 2017 | |
Diversely.io | Attraction | The tool finds and replaces biased language in job ads. It also automatically posts jobs to non-traditional job boards to reach more diverse talent. | Singapore | 1-10 | Pre-Seed | 1000000 | Crunchbase | 2020 | ||
JobWords | Attraction | Within Brand Attraction, the software finds exclusive language within job ads and flags them. It also connects to the employer's HR software to display modern and accessible job ads or job boards on the employer's website. These are specifically designed to increase accessibility for people with a disability. | Australia | 1-10 | Pre-Seed | 74000 | Crunchbase | 2018 | ||
Textio | Attraction | The tool finds and replaces biased language in job ads. It calculates a score for how inclusionary the language within job ads is which makes it easy for companies to improve. | Artificial Intelligence, Machine Learning, Natural Language Processing | United States | 101-250 | Venture - Series Unknown | 42500000 | Crunchbase | 2014 | |
TalVista | Attraction | For Brand Attraction, the tool can be used to flag and replace problematic language. | United States | 1-10 | 0 | Crunchbase | 2018 | |||
Tallocate | Attraction | Companies looking to employ diverse employees can post jobs on the platform. | Artificial Intelligence | United States | 11-50 | Corporate Round | 570000 | Crunchbase | 2020 | |
Mathison | Attraction | At the Brand Attraction stage, this tool finds and replaces exclusionary language in job-descriptions or other written communication. | United States | 11-50 | Seed | 6200000 | Crunchbase | 2019 | ||
Untapped | Attraction | Through analyzing the diversity of the sourcing funnel, the tool helps companies to target their branding towards underrepresented identities and groups. | Analytics | United States | 51-100 | Series C | 82700000 | Crunchbase | 2017 | |
SAP Success Factors | Attraction | The Job Analyzer Functionality within SAP Success Factors helps business create unbiased job desciptions. | Machine Learning | Germany | 10001+ | 0 | SAP Blog | 1972 | ||
ishield | Attraction | Within Brand Attraction, the tool analyzes marketing content for exclusionary language. | Artificial Intelligence, Machine Learning | United States | 1-10 | 0 | Crunchbase | 2021 | ||
InHerSight | Attraction | The tool provides a platform where job ads are focused on chracteristics of a company that are especially relevant to women. | United States | 1-10 | Convertible Note | 750000 | Crunchbase | 2014 | ||
Jopwell | Attraction | The platform connects, Black, Latinx and Native American Students with companies that want to increase their diversity hiring . | United States | 51-100 | Series A | 11900000 | Crunchbase | 2014 | ||
Applied | Attraction | The tool analyzes job ads for language that is potentially not inclusive and flags which groups might feel excluded. The tool uses AI to keep up with continuous language evolution. | United Kingdom | 11-50 | Seed | 4600000 | Crunchbase | 2015 | ||
Entelo | Attraction | The tool highlights non-inclusive language in job ads. | United States | 101-250 | Venture - Series Unknown | 40700000 | Crunchbase | 2011 | ||
The Mom Project | Attraction | The tool is a platform that connects moms to companies. | United States | 101-250 | Series C | 115600000 | Crunchbase | 2016 | ||
Witty Works | Attraction | The tool supports inclusive langauge through AI. | Switzerland | 11-50 | Pre-Seed | 843400 | Dealroom | 2018 | ||
Visier | Attraction | The tool helps a company to analyze what's important for diverse talent and understand the hiring funnel, so that the company can use this information to attract them. It analyzes applicant, candidate and employee data to show the success of hiring measures. It measures the quality of hires to see what a successful employee needs. It models scenarios to see what it will take to increase diversity and it collects content updates to inform a company of the demands of diverse talent needs. | Analytics | Canada | 251-500 | Series E | 216500000 | Crunchbase | 2010 | |
HireVue | Attraction | Hiring with this tool is based on video interviews where applicants record themselves in giving anaswers on a pre-determined structured interview. They also use games and coding interviews to further test candidates or for technical hiring. The AI sources candidates automatically through social media or the companys applicant tracking system. It then screens the candidates based on pre-determined objective criteria and lead candidates through the beginning of the hiring pipeline. | Artificial Intelligence, Machine Learning | United States | 251-500 | Private Equity | 93000000 | Crunchbase | 2004 | |
Yello | Attraction | The tool offers a job board that is specifically aimed at people from underrepresented groups. | United States | 101-250 | Venture - Series Unknown | 89400000 | Crunchbase | 2008 | ||
HireVue | Recruitment | Hiring with this tool is based on video interviews where applicants record themselves in giving anaswers on a pre-determined structured interview. They also use games and coding interviews to further test candidates or for technical hiring. The AI sources candidates automatically through social media or the companys applicant tracking system. It then screens the candidates based on pre-determined objective criteria and lead candidates through the beginning of the hiring pipeline. | Artificial Intelligence, Machine Learning | United States | 251-500 | Private Equity | 93000000 | Crunchbase | 2004 | |
Karat | Recruitment | This tool is specifically desgined for technical interviews. It is based on the research finding that women and underrepresented identities perform better in technical interviews when they are guided through them. Therefore, they give structured interview guidelines to Interview Engineers which they are held accountable to. Also, they only lead the technical interview, but don't do further interviewing based on a resume. Contrary to traditional code tests, this tool supports live technical interviews which have found to be more effective than traditional coding tests. | United States | 251-500 | Series C | 169100000 | Crunchbase | 2014 | ||
Lever | Recruitment | At the recruiting stage, companies can send diversity-focused surveys to people that have applied or have not applied to find out how diverse the hiring pipeline is or potential reasons why underrepresented talent has decided against applying. | Analytics | United States | 251-500 | Late Stage Venture | 122800000 | Crunchbase | 2012 | |
Retorio | Recruitment | This tool supports AI-powered interviews. Recruiters can determine the traits needed for a job based on the Big 5 model and candidates are evaluated accordingly to how strongly they show any of these Big 5 traits within their application video. To not introduce any biases into the software, the AI is supervised - which means that it only trains under human supervision - and is based on a diverse and broad data set (UCLA's Fairface dataset). | Artificial Intelligence, Machine Learning | Germany | 1-10 | Seed | 0 | Crunchbase | 2018 | |
Testhub | Recruitment | Recuiter gets an interview manual and instructions on how to perform a non-biased structured interview. | Norway | 1-10 | 0 | Crunchbase | 2019 | |||
Herman | Recruitment | The tool assesses candidates before they talk to a human recruiter based on non-demographic information. It works through analyzing a fully automated online interview. | Artificial Intelligence, Machine Learning, Analytics | United States | 1-10 | 0 | Crunchbase | 2017 | ||
Career.Place | Recruitment | With this tool, the employer gives pre-defined criteria, skills, scenario questions for candidates to qualify against. Candidates go through a funnel anonymously and if they qualify based on the employer's criteria, the employer is able to see the CV. | United States | 1-10 | 0 | Crunchbase | 2017 | |||
Equitas | Recruitment | The tool provides frameworks which are used as the basis for video interviews with candidates. Interviews can be conducted through phone, video and in-person, objectivity is secured through the structured and consistent frameworks. The platform also supports panel interviews with up to 4 interviewers for objective evaluation of the candidates. Evidence-based scoring can be conducted through assessing the audio and transcript of the video to endure that candidates are only evaluated based on the content they provide. Another feature that ensures fair and inclusive interviewing is collaboative scoring, which is supported for all of the interviewers. D&I-relevant demographic information is captured anonymously to track the progression of candidates. | Analytics | United Kingdom | 1-10 | 0 | Crunchbase | 2018 | ||
Quinn Cobbledger | Recruitment | Within recruitment, candidates can provide 60-second long video elevator pitches which they are evaluated based on. The elevator pitch format is supposed to reduce biases through the comparable structuring. | United States | 1-10 | Seed | 0 | Crunchbase | 2020 | ||
Propl AS | Recruitment | Recruiters can choose from games that fit the role they are hiring for. Candidates are then evaluated based on their performance in the games. | Artificial Intelligence | Norway | 1-10 | Grant | 17500 | Crunchbase | 2021 | |
Hubert | Recruitment | Conversational-AI platform that leads chats with candidates and reduces the number potentials before a human recruiter takes over. | Artificial Intelligence | Sweden | 1-10 | 0 | Crunchbase | 2016 | ||
Sigma Polaris | Recruitment | AI tool that pre-screens candidates based on their skills. | Artificial Intelligence, Machine Learning | United Kingdom | 1-10 | 0 | Crunchbase | 2019 | ||
Unbent | Recruitment | This is a recruiting tool which leverages Virtual Reality to create real-world scenarios where candidates simulate interaction with other people to test their soft skills. The platform builds and analysis of the candidates' behavior which companies can compare to their recruiting profile and to other candidates. | Artificial Intelligence, Analytics, Virtual Reality | United States | 1-10 | 0 | Crunchbase | 2020 | ||
Employa | Recruitment | Add-on for the ATS systems of companies, which checks resumes based on their skills through AI. | Artificial Intelligence | United States | 11-50 | 0 | Crunchbase | 2019 | ||
Fortay | Recruitment | Within recruitment, the tool focuses on the value fit of a person. It tests this based on a questionnaire that the applicant has to answer. | Artificial Intelligence | Canada | 1-10 | Non-equity Assistance | 0 | Crunchbase | 2015 | |
TalVista | Recruitment | The tool redacts any non-skills related information from a resume. The resume structure remains intact so that candidates can present themselves how they want. The tool also provides structured interview templates for recruiters. | United States | 1-10 | 0 | Crunchbase | 2018 | |||
Divercity.io | Recruitment | The tool offers diversity filters to look specifically for diverse candidates. | United States | 11-50 | Seed | 0 | Crunchbase | 2016 | ||
Wonderlic | Recruitment | The tool determines a score for each candidate based on their cognitive ability, personality and motivation. It does so based on questionnaires which were designed by I/O-psychologists. | United States | 51-100 | 0 | Crunchbase | 1937 | |||
OWIWI | Recruitment | The tool tests the soft skills of applicants within an online game. | Gaming | Greece | 1-10 | Non-equity Assistance | 551800 | Crunchbase | 2014 | |
MeVitae | Recruitment | The tool redacts any non-skills related information before it passes the applicants on to the recruiting companys' ATS. It then screens candidates for skills and shortlists them. | Artificial Intelligence, Analytics, Natural Language Processing | United Kingdom | 11-50 | Pre-Seed | 1700000 | Dealroom | 2015 | |
Harver | Recruitment | IO-psychologists work for the tool to determine qualities and competencies that are important for the job, so that recruiters have pre-determined metrics to come back to during the interview to make sure they are not biased. The tool also enables blind hiring to automatically analyze an applicants' objective information and match them to the qualities needed for the job. | Analytics | The Netherlands | 51-100 | Series B | 28900000 | Crunchbase | 2010 | |
Cangrade | Recruitment | The tool is based on unbiased psychometric pre-hire assessments, to select applicants before the interview process. To match applicants to an employer, personality attributes of the applicant and success metrics of employees at the company are matched through an ML algorithm to filter out promising candidates. | Artificial Intelligence, Machine Learning | United States | 11-50 | Seed | 525000 | Crunchbase | 2014 | |
Interviewer.AI | Recruitment | The tool pre-screens candidates automatically through AI. | Artificial Intelligence | Singapore | 11-50 | Seed | 612600 | Crunchbase | 2018 | |
GoodJob Software | Recruitment | The tool uses AI to automatically assess not only the skills and experiences of an applicant, but also their behaviors, traits and how they match to top performers in the company. Candidates answer questions and the AI generates the characteristics according to the path assessment. . | Artificial Intelligence, Machine Learning | United States | 11-50 | Seed | 3000000 | Crunchbase | 2019 | |
Diversely.io | Recruitment | In the Recruitment Stage, the tool anonymizes non-essential information from profiles. | Singapore | 1-10 | Pre-Seed | 1000000 | Crunchbase | 2020 | ||
🗞️ Sources
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