Diversity Metrics Tracking

Why rely solely on surveys when you can uncover the true state of DEI through concrete metrics? This is a question that echoes in my mind each time I embark on a new journey with a client. Surveys can provide valuable opinions, but they often fall short of capturing real facts and the nuanced realities of individuals within an organization. 🔎 Here are 6 key DEI metrics that truly matter: 📍 Attrition Rates: Take a closer look at why employees are leaving, especially among different groups. This will help you understand if there are specific challenges or issues that need to be addressed to improve retention. 📍 Leadership Pipeline Diversity: Evaluate the diversity within your leadership team. Are there opportunities for underrepresented individuals to rise into leadership roles? Are they equally represented on all levels of leadership? 📍 Promotion and Advancement Rates: Assess if all employees, regardless of background, are getting equal opportunities to advance in their careers. By monitoring promotion and advancement rates, you can identify any biases and work towards creating a level playing field. 📍 Pay Equity: Ensure that everyone is paid fairly and equally for their work. Address any discrepancies in pay based on not only gender, but also race, age, ethnicity or other intersectional factors. 📍 Hiring Pipeline Diversity: Examine the diversity of candidates in your hiring process. Are you attracting a wide range of talent from different backgrounds? Tracking this metric helps you gauge the effectiveness of your recruitment efforts in creating a diverse workforce. 📍 Employee Engagement by Demographic: Measure the level of engagement and satisfaction among employees from various groups. Are there any disparities in engagement levels? Run the crossings of identity diversity and organizational one. By focusing on these 6 concrete metrics, you can gain real insights into your organization's DEI progress based on actionable data that drives progress. ________________________________________ Are you looking for more HR tips and DEI content like this?  📨 Join my free DEI Newsletter: https://lnkd.in/dtgdB6XX

AI systems built without women's voices miss half the world and actively distort reality for everyone. On International Women's Day - and every day - this truth demands our attention. After more than two decades working at the intersection of technological innovation and human rights, I've observed a consistent pattern: systems designed without inclusive input inevitably encode the inequalities of the world we have today, incorporating biases in data, algorithms, and even policy. Building technology that works requires our shared participation as the foundation of effective innovation. The data is sobering: women represent only 30% of the AI workforce and a mere 12% of AI research and development positions according to UNESCO's Gender and AI Outlook. This absence shapes the technology itself. And a UNESCO study on Large Language Models (LLMs) found persistent gender biases - where female names were disproportionately linked to domestic roles, while male names were associated with leadership and executive careers. UNESCO's @women4EthicalAI initiative, led by the visionary and inspiring Gabriela Ramos and Dr. Alessandra Sala, is fighting this pattern by developing frameworks for non-discriminatory AI and pushing for gender equity in technology leadership. Their work extends the UNESCO Recommendation on the Ethics of AI, a powerful global standard centering human rights in AI governance. Today's decision is whether AI will transform our world into one that replicates today's inequities or helps us build something better. Examine your AI teams and processes today. Where are the gaps in representation affecting your outcomes? Document these blind spots, set measurable inclusion targets, and build accountability systems that outlast good intentions. The technology we create reflects who creates it - and gives us a path to a better world. #InternationalWomensDay #AI #GenderBias #EthicalAI #WomenInAI #UNESCO #ArtificialIntelligence The Patrick J. McGovern Foundation Mariagrazia Squicciarini Miriam Vogel Vivian Schiller Karen Gill Mary Rodriguez, MBA Erika Quada Mathilde Barge Gwen Hotaling Yolanda Botti-Lodovico

New research from Aymara evaluated gender bias across 13 popular image generators and found a concerning pattern: most don't just reflect gender stereotypes, they tend to amplify them. When asked to generate images of professionals without specifying gender, the average model showed men 93% of the time for stereotypically male professions and 68% for gender-neutral roles. I'm encouraged that Amazon Nova Canvas showed the lowest bias and achieved representation close to parity. This feels like meaningful validation of our teams’ relentless focus on responsible AI, and I'm grateful to every team member who's contributed to this progress…though we know there’s always more work to do. You can read the full report here: https://lnkd.in/e3-eCXdi

Women make up 52% of entry-level (P1) employees but just 23% of C-Suite executives. Why do so few women make it to senior roles? There is a lot of emphasis in the news on the gender pay gap. However, this “unadjusted” gender pay gap typically fails to capture what is perhaps the most powerful underlying force holding back women–𝗶𝗻𝗰𝗿𝗲𝗮𝘀𝗶𝗻𝗴 𝗱𝗶𝘀𝗽𝗮𝗿𝗶𝘁𝗶𝗲𝘀 𝗶𝗻 𝗿𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗿𝗮𝘁𝗲𝘀 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝘄𝗼𝗺𝗲𝗻 𝗮𝗻𝗱 𝗺𝗲𝗻 𝗮𝘁 𝘀𝗲𝗻𝗶𝗼𝗿 𝗮𝗻𝗱 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲 𝗹𝗲𝘃𝗲𝗹𝘀. Today, let’s test a few hypotheses for why this gap becomes so pronounced at senior levels. _______________ From first principles, there are three potential ways in which women-vs-men representation divergences can happen at senior levels: Potential Force 1: disparities in 𝗽𝗿𝗼𝗺𝗼𝘁𝗶𝗼𝗻 𝗿𝗮𝘁𝗲𝘀 between genders Potential Force 2: disparities in 𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗲 𝘁𝘂𝗿𝗻𝗼𝘃𝗲𝗿 𝗿𝗮𝘁𝗲𝘀 between genders Potential Force 3: disparities in 𝗵𝗶𝗿𝗶𝗻𝗴 𝗿𝗮𝘁𝗲𝘀 between genders by job level Let’s take a look at all three potential forces to understand what is happening underneath the hood in the labor market. _______________ 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗙𝗼𝗿𝗰𝗲 𝟭: 𝗱𝗶𝘀𝗽𝗮𝗿𝗶𝘁𝗶𝗲𝘀 𝗶𝗻 𝗽𝗿𝗼𝗺𝗼𝘁𝗶𝗼𝗻 𝗿𝗮𝘁𝗲𝘀 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗴𝗲𝗻𝗱𝗲𝗿𝘀 🟡 Conclusion: For the most part, promotion rates are largely consistent for men and women across all job levels. I would thus suggest that promotion rate disparities are not the main driver of the representation rate divergence for men vs women at senior levels. _______________ 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗙𝗼𝗿𝗰𝗲 𝟮: 𝗱𝗶𝘀𝗽𝗮𝗿𝗶𝘁𝗶𝗲𝘀 𝗶𝗻 𝗲𝗺𝗽𝗹𝗼𝘆𝗲𝗲 𝘁𝘂𝗿𝗻𝗼𝘃𝗲𝗿 𝗿𝗮𝘁𝗲𝘀 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗴𝗲𝗻𝗱𝗲𝗿𝘀 🔴 Conclusion: Employee turnover is slightly-but-not-massively higher for women across all job levels. Thus, I would suggest that turnover rate disparities are perhaps a small driver of the representation rate divergence for men vs. women at senior levels. _______________ 𝗣𝗼𝘁𝗲𝗻𝘁𝗶𝗮𝗹 𝗙𝗼𝗿𝗰𝗲 𝟯: 𝗱𝗶𝘀𝗽𝗮𝗿𝗶𝘁𝗶𝗲𝘀 𝗶𝗻 𝗵𝗶𝗿𝗶𝗻𝗴 𝗿𝗮𝘁𝗲𝘀 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗴𝗲𝗻𝗱𝗲𝗿𝘀 𝗯𝘆 𝗷𝗼𝗯 𝗹𝗲𝘃𝗲𝗹 🔴🔴🔴 Conclusion: Hiring rate disparities between men and women by job level are where we see substantial gaps. For instance, women represent 51.5% of recent P1 hires but only 23.2% of recent C-Suite hires–a 29 percentage point gap. Thus, I would suggest that 𝘁𝗵𝗲 𝗽𝗿𝗶𝗺𝗮𝗿𝘆 𝗱𝗿𝗶𝘃𝗲𝗿 𝗼𝗳 𝗿𝗲𝗽𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻 𝗿𝗮𝘁𝗲 𝗱𝗶𝘃𝗲𝗿𝗴𝗲𝗻𝗰𝗲 𝗳𝗼𝗿 𝗺𝗲𝗻 𝘃𝘀. 𝘄𝗼𝗺𝗲𝗻 𝗮𝘁 𝘀𝗲𝗻𝗶𝗼𝗿 𝗹𝗲𝘃𝗲𝗹𝘀 𝗮𝗽𝗽𝗲𝗮𝗿𝘀 𝘁𝗼 𝗯𝗲 𝘁𝗵𝗲 𝗵𝗶𝗿𝗶𝗻𝗴 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗶𝘁𝘀𝗲𝗹𝗳. _______________ 𝗖𝗮𝘃𝗲𝗮𝘁𝘀: Pave’s dataset skews largely to the tech sector. Also, due to sample size constraints, individuals identifying as non-binary or other distinctions are not included in this analysis.

International Women's Day is about celebrating the social, economic, cultural and political achievements of women. It also marks a call to action for accelerating gender parity – including in the workplace.   LinkedIn data shows that despite decades of progress for women at work, they are still underrepresented at the highest levels of companies. Women may hold 42% of jobs globally, but they occupy less than a third of leadership positions.   In the Middle East, women remain largely underrepresented in leadership positions. The trend for women in leadership in the UAE, for example, is 22%, lagging behind the global average. Since 2016, however, there has been progress in the country, with an increase of two percentage points. Globally, there's also been progress, with more women being appointed to senior roles, accounting for 37% of all leadership hires in 2023, up from 33% in 2016. However, LinkedIn's Economic Graph suggests that women fare worse during economic uncertainty, with fewer women hired for senior roles when job market conditions deteriorate. In 2020, at the height of the Covid pandemic, the data shows that the share of hiring women into leadership roles in the UAE stagnated (24%) before continuing on an upward trajectory reaching 27% in 2023. Despite progress, there’s still a long way to go to close the gender gap, with overall women representation across almost all sectors in the UAE falling under the 50% mark. Even industries where women either make up the majority of the workforce or nearly half of it, they are underrepresented in leadership. In education, for example, women account for 52% of the workforce but 41% of leadership positions. Similarly, in the hospitals and healthcare industry, women represent 49% of the workforce but only 31% of leaders.   The data clearly illustrates the 'broken rung' phenomenon, where we see a diminishing number of women as we move up each level of the hierarchy. In many countries, including the UAE, the first significant drop in women's representation occurs between the senior contributor and manager levels. While in the UAE there is a marginal three-percent increase in women’s representation between manager and director levels, it consistently then declines, culminating in a sharp decrease in the number of women in the C-suite.    To accelerate positive change, employers can boost women’s representation through a skills-first hiring approach. LinkedIn's 2023 Skills First report found embracing such a policy significantly increases the number of workers in talent pools, particularly for women. Measures like flexible schedules and parental leave can also enhance inclusivity. What other strategies can be employed to strengthen women's representation in leadership? Join the conversation. #LinkedInNewsMiddleEast #IWD24 #InternationalWomensDay   Reported by Dana Moukhallati Insights by Silvia Lara (LinkedIn's Economic Graph)    Sources https://lnkd.in/gtPZ_T2X https://lnkd.in/gW4sRyqE 📷 Getty

We are excited to share a research paper co-authored by WFP India's Paramjyoti Chattopadhyay, Head of the Research, Assessment, Monitoring, and Evaluation Unit, and Vijay Avinandan, Monitoring and Evaluation Officer, published in the Asia Pacific Journal of Evaluation on how evaluations can drive inclusive policymaking. Some the questions the paper, looks at are: 1. Can instilling the dimension of equity and inclusivity in evaluative choices, approaches, and frameworks, nudge social security schemes and policymakers to be instinctively responsive to the needs of vulnerable populations? 2. Can evaluations nudge policy makers to pay more attention to equity and inclusion considerations? Read https://lnkd.in/gGXtrRRY #nudge-effect #evaluation #systems #equity #evaluations #inclusion

Given heightened marketplace uncertainty and transparency, organizations are struggling to quantify the impact of their inclusion work. My ally Victoria Mattingly recommends these ideas to start: 1. Hiring Data: Review hiring data at each stage of the selection process to see if historically marginalized candidates are not advancing. This can help an organization discover if inclusive hiring practices need to be revisited and ensure the candidate pool is diverse from the start. 2. Retention and Promotion Rates: Analyze retention and promotion rates to see if employees from historically marginalized groups are leaving at higher rates or are consistently passed over for advancement. This data could signal a need for more inclusive performance management processes and help a company understand if its culture is one where all employees can thrive. 3. Pay Equity Audits: Conduct comprehensive pay equity audits to identify disparities in compensation across gender, race, and other identity markers. While this might feel risky or even costly, the financial and reputational cost of a discrimination lawsuit is far greater. As Mattingly points out, pay inequality is a primary driver of turnover and can be a significant drag on a company’s bottom line. 4. Performance Evaluation Data: Combing through performance evaluation data can uncover patterns of bias if certain groups of employees are consistently rated lower or receive fewer growth opportunities. If the data shows a consistent pattern of lower scores for a particular demographic, it's a clear signal that bias may be influencing evaluations. 5. Leadership and Development Tracking: Track participation in leadership development programs, sponsorship initiatives, and high-visibility projects. This serves as a proactive indicator of whether all employees have access to advancement pathways. If the same groups of people are consistently getting these opportunities, it's a sign that the playing field isn't level. Full piece here: https://lnkd.in/gtTQ7yQf #inclusion #culture #leadership

How does your organization's DEI initiatives measure up? DEI expert Lee Jourdan highlights seven crucial metrics that span the entire employee life cycle, offering a true gauge of your DEI progress, and whether your organization is truly a meritocracy. From assessing attrition rates and performance to examining pay equity and inclusion, these indicators provide a view into whether your company is living up to its DEI promises. As Jourdan writes, "We know that what gets measured gets done. We also know that transparent data provides one version of the truth and helps organizations determine priorities." Read the full article here: #InclusionMatters #DEIprogress #Diversity #Equity Image alt-text: Bars of various colors and lengths arranged in a half circle.

Managing AI bias using CRISP-DM…and why even you have to know about this One of the key risks of AI is ‘garbage in garbage out', which means that if the data is biased, it is very likely that the system's output will exhibit biases which could create significant risks for companies. Example. A job searching platform offered more results when the search term was. “chercheur” (the masculine term for “researcher” in French) compared to the search term “chercheuse” (the feminine term for “researcher” in French). This anecdotal example demonstrates the kind of impact that a biased distribution of valuable information can have. A second example is a job seeker profiling system developed in Austria. The system divides job seekers into three categories: Group A - High prospects to find employment in the short term; Group B - Mediocre prospects, meaning job seekers not part of groups A or C; Group C - Low prospects to find employment in the long-term. Austria aimed to streamline resource allocation, make job search assistance more efficient by targeting Group B – given that group A was perceived as not problematic and that group C was considered as entailing a too high cost compared to the benefits. The system, however, incorporated and affected a negative weight to data such as gender and migration background to 'reflect the harsh reality of the labour market'. This can create a negative loop whereby discrimination patterns in society are encoded in the distribution of valuable resources such as training or support for labour market integration. The European Labour Agency published a report that addresses discrimination, bias and other ethical issues that arise from data mining. They approacht the mitigation by building in bias mitigations in every step of the so-called CRISP-DM process. CRISP-DM is a commonly used data science life cycle framework. CRISP-DM stands for Cross-Industry Standard Process for Data Mining. The CRISP-DM life cycle includes six phases: Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. For each phase a clear mitigation is defined. Below model provides a good foundation for owners of business processes that use AI models or buyers of AI. Something that is one of that new elements of expertise we all have to have in the new future of work. See report: https://lnkd.in/e6FPbXra

For those involved in evaluating or measuring impact around social inclusion, this guidance note on Reporting on Disability Inclusion from Pacific Women Lead might be useful. Collecting and sharing information is categorised in this by 3 types: Preconditions: The extent to which necessary accommodations, such as access to support services, assistive devices, and accessible venues, have been provided to enable the active participation of women and girls with disabilities in various initiatives. This also includes the extent there is working in partnership with Organisations of Persons with Disabilities (OPDs) to inform the development and implementation of disability-inclusive initiatives. Inclusion: The degree to which an activity has reached and involved people with disabilities, through the collection and disaggregation of quantitative data. Participation: the extent to which people with disabilities have been meaningfully and actively involved in initiatives and benefitted from these. See especially the good summary diagram with indicators on p5. #inclusion #socialimpact #evaluation