{"id":169,"date":"2024-10-19T03:33:51","date_gmt":"2024-10-19T03:33:51","guid":{"rendered":"https:\/\/superturtleai.com\/blog\/10-strategies-to-mitigate-ai-bias-in-hiring\/"},"modified":"2024-10-25T11:13:08","modified_gmt":"2024-10-25T11:13:08","slug":"10-strategies-to-mitigate-ai-bias-in-hiring","status":"publish","type":"post","link":"https:\/\/superturtleai.com\/blog\/10-strategies-to-mitigate-ai-bias-in-hiring\/","title":{"rendered":"10 Strategies to Mitigate AI Bias in Hiring"},"content":{"rendered":"<p>AI is transforming hiring, but it can perpetuate unfair biases. Here are 10 ways to reduce AI bias in recruitment:<\/p>\n<ol>\n<li>Use diverse training data<\/li>\n<li>Try blind recruiting<\/li>\n<li>Check algorithms regularly<\/li>\n<li>Make AI decisions clear<\/li>\n<li>Build diverse AI teams<\/li>\n<li>Use fair AI algorithms<\/li>\n<li>Keep human oversight<\/li>\n<li>Train staff on AI and bias<\/li>\n<li>Ask candidates for feedback<\/li>\n<li>Set ethical AI guidelines<\/li>\n<\/ol>\n<p>Quick comparison of AI hiring pros and cons:<\/p>\n<table style=\"width:100%;\">\n<thead>\n<tr>\n<th>Pros<\/th>\n<th>Cons<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Speeds up hiring<\/td>\n<td>Potential for bias<\/td>\n<\/tr>\n<tr>\n<td>Better candidate experience<\/td>\n<td>May miss good candidates<\/td>\n<\/tr>\n<tr>\n<td>Reduces paperwork<\/td>\n<td>Lacks human touch<\/td>\n<\/tr>\n<tr>\n<td>Handles more applications<\/td>\n<td>Possible legal issues<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>AI bias in hiring occurs when AI tools make unfair decisions about job candidates, often unintentionally favoring certain groups. This can lead to legal issues, hurt diversity efforts, and worsen existing inequalities.<\/p>\n<p>Companies face challenges in reducing AI bias, including technical hurdles, internal resistance, and balancing speed with fairness. However, new AI tools and regulations are emerging to help make hiring more equitable.<\/p>\n<p>To build fairer AI hiring systems, companies should use diverse data, remove identifying info, regularly audit AI, keep humans involved, and establish clear ethics guidelines.<\/p>\n<h2 id=\"related-video-from-youtube\" tabindex=\"-1\" class=\"sb\">Related video from YouTube<\/h2>\n<p><iframe class=\"sb-iframe\" src=\"https:\/\/www.youtube-nocookie.com\/embed\/YB6n2vP8msE\" frameborder=\"0\" loading=\"lazy\" allowfullscreen style=\"width: 100%; height: auto; aspect-ratio: 16\/9;\"><\/iframe><\/p>\n<h2 id=\"what-is-ai-bias-in-hiring%3F\" tabindex=\"-1\" class=\"sb\">What is AI bias in hiring?<\/h2>\n<p>AI bias in hiring happens when AI tools make unfair decisions about job candidates. These tools can favor some groups over others, often unintentionally.<\/p>\n<h3 id=\"defining-ai-bias\" tabindex=\"-1\">Defining AI bias<\/h3>\n<p>AI bias occurs when an AI system produces results that unfairly favor or discriminate against certain groups. In hiring, this can mean:<\/p>\n<ul>\n<li>Favoring male candidates over female ones<\/li>\n<li>Preferring younger applicants to older ones<\/li>\n<li>Recommending candidates from specific racial backgrounds more often<\/li>\n<\/ul>\n<p>These biases can sneak in through flawed training data, poor algorithm design, or human prejudices baked into the system.<\/p>\n<h3 id=\"types-of-ai-bias\" tabindex=\"-1\">Types of AI bias<\/h3>\n<p>AI bias in hiring comes in different flavors:<\/p>\n<table style=\"width:100%;\">\n<thead>\n<tr>\n<th>Type<\/th>\n<th>Description<\/th>\n<th>Example<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Historical<\/td>\n<td>Reflects past prejudices<\/td>\n<td>AI favors male candidates due to past hiring patterns<\/td>\n<\/tr>\n<tr>\n<td>Sample<\/td>\n<td>Training data lacks diversity<\/td>\n<td>AI struggles with non-white, non-male candidates<\/td>\n<\/tr>\n<tr>\n<td>Algorithmic<\/td>\n<td>AI design reinforces biases<\/td>\n<td>AI scores resumes with male-associated words higher<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Take <a href=\"https:\/\/www.aboutamazon.com\/\" target=\"_blank\" style=\"display: inline;\" rel=\"noopener\">Amazon<\/a>&#8216;s AI hiring tool. It penalized resumes from women because it learned from <a href=\"https:\/\/www.aboutamazon.com\/\" target=\"_blank\" style=\"display: inline;\" rel=\"noopener\">Amazon<\/a>&#8216;s male-dominated hiring history. They had to scrap the whole thing.<\/p>\n<blockquote>\n<p>&quot;Every system that involves human beings is biased in some way, because as a species we are inherently biased.&quot; &#8211; Donncha Carroll, Partner at Axiom Consulting Partners<\/p>\n<\/blockquote>\n<p>This quote nails it: our biases can seep into AI systems, making them mirror and amplify our prejudices.<\/p>\n<p>Another eye-opener? <a href=\"https:\/\/www.onlineenglishteaching.com\/itutor\" target=\"_blank\" style=\"display: inline;\" rel=\"noopener\">iTutorGroup<\/a>&#8216;s $365,000 settlement with the <a href=\"https:\/\/www.eeoc.gov\/\" target=\"_blank\" style=\"display: inline;\" rel=\"noopener\">Equal Employment Opportunity Commission<\/a>. Their AI-driven application review software showed bias against older applicants.<\/p>\n<p>These cases show why tackling AI bias in hiring matters. It&#8217;s not just about fairness\u2014it&#8217;s a legal and ethical must for companies.<\/p>\n<h2 id=\"how-ai-bias-affects-hiring\" tabindex=\"-1\" class=\"sb\">How AI bias affects hiring<\/h2>\n<p>AI bias in hiring is a big deal. It can cause problems for companies and job seekers alike.<\/p>\n<h3 id=\"legal-and-ethical-issues\" tabindex=\"-1\">Legal and ethical issues<\/h3>\n<p>AI bias can land companies in hot water:<\/p>\n<ul>\n<li>iTutorGroup got sued for $365,000 in 2022. Why? Their AI apparently filtered out older applicants.<\/li>\n<li>Amazon had to ditch their AI hiring tool in 2018. It didn&#8217;t like women applicants.<\/li>\n<\/ul>\n<p>These aren&#8217;t just unfair practices. They&#8217;re illegal. Companies using biased AI risk lawsuits and a tarnished reputation.<\/p>\n<h3 id=\"impact-on-workplace-diversity\" tabindex=\"-1\">Impact on workplace diversity<\/h3>\n<p>Biased AI can mess up diversity efforts:<\/p>\n<table style=\"width:100%;\">\n<thead>\n<tr>\n<th>Problem<\/th>\n<th>Result<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Filtered candidates<\/td>\n<td>Good diverse applicants get rejected<\/td>\n<\/tr>\n<tr>\n<td>Repeated old patterns<\/td>\n<td>AI learns from past hiring, keeps old biases<\/td>\n<\/tr>\n<tr>\n<td>Missed talent<\/td>\n<td>Companies lose out on skills from diverse groups<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>A 2018 study found AI thought Black applicants looked more negative than white ones. This kind of bias leads to fewer diverse hires.<\/p>\n<h3 id=\"worsening-existing-inequalities\" tabindex=\"-1\">Worsening existing inequalities<\/h3>\n<p>AI bias doesn&#8217;t just keep things the same. It can make them worse:<\/p>\n<ul>\n<li>If AI likes certain groups, they get more chances over time.<\/li>\n<li>It&#8217;s harder for underrepresented groups to get in.<\/li>\n<li>The gap between privileged and marginalized groups grows.<\/li>\n<\/ul>\n<p>Trey Causey from <a href=\"https:\/\/www.indeed.com\/\" target=\"_blank\" style=\"display: inline;\" rel=\"noopener\">Indeed<\/a> puts it well:<\/p>\n<blockquote>\n<p>&quot;These models were trained on data produced by humans, right? So like all of the things that make humans human \u2014 the good and the less good \u2014 those things are going to be in that data.&quot;<\/p>\n<\/blockquote>\n<p>So, AI can make human biases even stronger.<\/p>\n<p>With 79% of employers using AI in hiring, fixing these issues is crucial for fair and legal recruitment.<\/p>\n<h2 id=\"10-ways-to-reduce-ai-bias-in-hiring\" tabindex=\"-1\" class=\"sb\">10 ways to reduce AI bias in hiring<\/h2>\n<p>AI can boost hiring efficiency, but it&#8217;s not immune to bias. Here&#8217;s how to minimize AI bias in your hiring:<\/p>\n<h3 id=\"1.-use-diverse-training-data\" tabindex=\"-1\">1. Use diverse training data<\/h3>\n<p>Feed your AI a wide range of data. This helps it learn from different groups.<\/p>\n<p>Amazon&#8217;s AI hiring tool once favored men because it learned from mostly male resumes. Avoid this by using data from various genders, races, and ages.<\/p>\n<h3 id=\"2.-try-blind-recruiting\" tabindex=\"-1\">2. Try blind recruiting<\/h3>\n<p>Strip personal info from applications. Let AI focus on skills, not demographics.<\/p>\n<table style=\"width:100%;\">\n<thead>\n<tr>\n<th>Before<\/th>\n<th>After<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Jane Smith, 35, Female<\/td>\n<td>Candidate #12345<\/td>\n<\/tr>\n<tr>\n<td>Skills: Python, SQL<\/td>\n<td>Skills: Python, SQL<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3 id=\"3.-check-algorithms-regularly\" tabindex=\"-1\">3. Check algorithms regularly<\/h3>\n<p>Test your AI often to catch biases. Set up quarterly audits.<\/p>\n<blockquote>\n<p>&quot;Quarterly audits of our AI systems increased our diverse hires.&quot; &#8211; John Xie, CEO at Taskade<\/p>\n<\/blockquote>\n<h3 id=\"4.-make-ai-decisions-clear\" tabindex=\"-1\">4. Make AI decisions clear<\/h3>\n<p>Use AI that explains its choices. It helps spot potential biases.<\/p>\n<h3 id=\"5.-build-diverse-ai-teams\" tabindex=\"-1\">5. Build diverse AI teams<\/h3>\n<p>Include different backgrounds when creating AI. It brings in various viewpoints.<\/p>\n<h3 id=\"6.-use-fair-ai-algorithms\" tabindex=\"-1\">6. Use fair AI algorithms<\/h3>\n<p>Pick AI tools designed for fairness. They aim to treat all groups equally.<\/p>\n<h3 id=\"7.-keep-human-oversight\" tabindex=\"-1\">7. Keep human oversight<\/h3>\n<p>Don&#8217;t let AI decide everything. Have people review AI choices.<\/p>\n<h3 id=\"8.-train-staff-on-ai-and-bias\" tabindex=\"-1\">8. Train staff on AI and bias<\/h3>\n<p>Teach your HR team about AI and bias. It helps them use AI tools fairly.<\/p>\n<h3 id=\"9.-ask-candidates-for-feedback\" tabindex=\"-1\">9. Ask candidates for feedback<\/h3>\n<p>Get input from job seekers on your AI hiring. It can reveal hidden biases.<\/p>\n<blockquote>\n<p>&quot;Candidate feedback on our AI hiring system led to a 15% increase in diversity hires and a 20% drop in bias complaints within a year.&quot; &#8211; Martyna Jasinska, HR Specialist at ePassportPhoto<\/p>\n<\/blockquote>\n<h3 id=\"10.-set-ethical-ai-guidelines\" tabindex=\"-1\">10. Set ethical AI guidelines<\/h3>\n<p>Create rules for AI in hiring. It clarifies what&#8217;s acceptable in AI recruitment.<\/p>\n<h6 id=\"sbb-itb-d5be3ae\" class=\"sb-banner\" style=\"color:transparent!important;line-height:0!important;padding:0!important;margin:0!important;\">sbb-itb-d5be3ae<\/h6>\n<h2 id=\"difficulties-in-reducing-ai-bias\" tabindex=\"-1\" class=\"sb\">Difficulties in reducing AI bias<\/h2>\n<p>Reducing AI bias in hiring isn&#8217;t a walk in the park. Companies face real challenges when trying to make their AI tools fair.<\/p>\n<h3 id=\"technical-challenges\" tabindex=\"-1\">Technical challenges<\/h3>\n<p>AI can pick up biases at multiple stages:<\/p>\n<ul>\n<li>Problem framing<\/li>\n<li>Data collection<\/li>\n<li>Data preparation<\/li>\n<li>Model building<\/li>\n<\/ul>\n<p>These biases often hide during development. Take Amazon&#8217;s AI recruiting tool. It favored men because it learned from past hiring data that was mostly male. The result? Amazon had to trash the whole thing.<\/p>\n<h3 id=\"company-pushback\" tabindex=\"-1\">Company pushback<\/h3>\n<p>Some firms drag their feet on reducing AI bias:<\/p>\n<ul>\n<li>&quot;It&#8217;s too expensive!&quot;<\/li>\n<li>&quot;It&#8217;ll slow down our hiring!&quot;<\/li>\n<li>&quot;Our AI isn&#8217;t biased&#8230; right?&quot;<\/li>\n<\/ul>\n<p>Here&#8217;s a wake-up call: 43% of job seekers think AI tools are MORE biased than human recruiters. That&#8217;s a trust gap companies can&#8217;t ignore.<\/p>\n<h3 id=\"balancing-speed-and-fairness\" tabindex=\"-1\">Balancing speed and fairness<\/h3>\n<p>Companies want to hire fast AND fair. But that&#8217;s tricky:<\/p>\n<table style=\"width:100%;\">\n<thead>\n<tr>\n<th>Speed<\/th>\n<th>Fairness<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Quick screening<\/td>\n<td>Thorough bias checks<\/td>\n<\/tr>\n<tr>\n<td>Automated decisions<\/td>\n<td>Human oversight<\/td>\n<\/tr>\n<tr>\n<td>Huge applicant pools<\/td>\n<td>Quality, diverse data<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Hilke Schellmann, author of &quot;The Algorithm&quot;, puts it bluntly:<\/p>\n<blockquote>\n<p>&quot;One biased human hiring manager can harm a lot of people in a year, and that&#8217;s not great. But an algorithm that is maybe used in all incoming applications at a large company\u2026 that could harm hundreds of thousands of applicants.&quot;<\/p>\n<\/blockquote>\n<p>So, how can companies balance speed and fairness?<\/p>\n<p>1. Use AI for initial screening, but have humans make final calls<\/p>\n<p>2. Regularly audit AI systems for bias<\/p>\n<p>3. Focus on data quality, not just quantity<\/p>\n<p>It&#8217;s not easy, but it&#8217;s necessary. The future of fair hiring depends on it.<\/p>\n<h2 id=\"what's-next-for-ai-in-hiring\" tabindex=\"-1\" class=\"sb\">What&#8217;s next for AI in hiring<\/h2>\n<p>AI is shaking up how companies find and hire talent. Here&#8217;s what&#8217;s on the horizon for AI recruitment tools and regulations.<\/p>\n<h3 id=\"new-ai-hiring-tools\" tabindex=\"-1\">New AI hiring tools<\/h3>\n<p>AI tools are getting better at finding the right people for jobs:<\/p>\n<ul>\n<li><strong><a href=\"https:\/\/www.seekout.com\/\" target=\"_blank\" style=\"display: inline;\" rel=\"noopener\">SeekOut<\/a><\/strong>: Finds diverse candidates by searching public profiles with smart filters.<\/li>\n<li><strong><a href=\"https:\/\/www.herohunt.ai\/\" target=\"_blank\" style=\"display: inline;\" rel=\"noopener\">HeroHunt.ai<\/a><\/strong>: Makes job outreach feel personal with automated first contacts and screenings.<\/li>\n<li><strong><a href=\"https:\/\/hirelogic.com\/features\/instant-interview-insights\/\" target=\"_blank\" style=\"display: inline;\" rel=\"noopener\">HireLogic<\/a><\/strong>: Gives real-time interview feedback to help avoid bias.<\/li>\n<\/ul>\n<p>These tools aim to speed up hiring and make it fairer. <a href=\"https:\/\/www.unilever.com\/\" target=\"_blank\" style=\"display: inline;\" rel=\"noopener\">Unilever<\/a>, for example, uses <a href=\"https:\/\/www.hirevue.com\/\" target=\"_blank\" style=\"display: inline;\" rel=\"noopener\">HireVue<\/a> to match candidate interviews with top employee traits.<\/p>\n<h3 id=\"changes-in-ai-ethics-and-laws\" tabindex=\"-1\">Changes in AI ethics and laws<\/h3>\n<p>As AI grows in hiring, so do fairness concerns. New rules are coming:<\/p>\n<table style=\"width:100%;\">\n<thead>\n<tr>\n<th>Area<\/th>\n<th>Change<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td>Transparency<\/td>\n<td>Companies may need to disclose AI use to candidates<\/td>\n<\/tr>\n<tr>\n<td>Bias checks<\/td>\n<td>Regular AI system audits for fair decisions<\/td>\n<\/tr>\n<tr>\n<td>Data protection<\/td>\n<td>Tighter rules on AI handling of personal info<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>The <a href=\"https:\/\/www.europarl.europa.eu\/topics\/en\/article\/20230601STO93804\/eu-ai-act-first-regulation-on-artificial-intelligence\" target=\"_blank\" style=\"display: inline;\" rel=\"noopener\">EU AI Act<\/a> is leading the charge, setting standards for safe and fair AI use in hiring.<\/p>\n<p>Some companies are getting ready:<\/p>\n<ul>\n<li>40% of HR teams are checking if their AI tools follow new rules<\/li>\n<li>16% have brought in outside help to review their AI systems<\/li>\n<\/ul>\n<p>But there&#8217;s still work to do. Only 27% of companies focus on making their AI trustworthy and less biased.<\/p>\n<p>As AI hiring grows, expect more laws to keep it in check. Companies will need to balance quick hiring with careful AI use to stay on the right side of the law and ethics.<\/p>\n<h2 id=\"conclusion\" tabindex=\"-1\" class=\"sb\">Conclusion<\/h2>\n<p>AI in hiring is a double-edged sword. It can speed things up, but AI bias is a real problem. Here&#8217;s how companies can build fairer AI hiring systems:<\/p>\n<p>1. <strong>Use diverse training data<\/strong><\/p>\n<p>AI learns from what you feed it. Garbage in, garbage out. Companies should:<\/p>\n<ul>\n<li>Mix up their candidate pool in training data<\/li>\n<li>Ditch old, potentially biased hiring info<\/li>\n<li>Keep data fresh to match today&#8217;s workforce<\/li>\n<\/ul>\n<p>2. <strong>Go blind<\/strong><\/p>\n<p>Hide stuff that might trigger bias:<\/p>\n<ul>\n<li>Strip names, photos, ages, and gender from resumes<\/li>\n<li>Use IDs instead of names early on<\/li>\n<li>Focus on skills, not personal details<\/li>\n<\/ul>\n<p>3. <strong>Check your AI<\/strong><\/p>\n<p>Don&#8217;t set it and forget it:<\/p>\n<ul>\n<li>Look for weird patterns in hiring decisions every few months<\/li>\n<li>Test your AI with fake candidates<\/li>\n<li>Keep an eye on diversity numbers over time<\/li>\n<\/ul>\n<p>4. <strong>Humans still matter<\/strong><\/p>\n<p>Don&#8217;t let AI run the show:<\/p>\n<ul>\n<li>Have real people double-check AI&#8217;s picks<\/li>\n<li>Let candidates ask for a human review if AI says no<\/li>\n<li>Train your team to spot AI acting up<\/li>\n<\/ul>\n<p>5. <strong>Set ground rules<\/strong><\/p>\n<p>Make a playbook for using AI right:<\/p>\n<ul>\n<li>Define what &quot;fair&quot; means for your company<\/li>\n<li>Have a plan for handling bias complaints<\/li>\n<li>Hold AI companies responsible for their tools<\/li>\n<\/ul>\n<h2 id=\"faqs\" tabindex=\"-1\" class=\"sb\">FAQs<\/h2>\n<h3 id=\"how-to-reduce-bias-in-ai-hiring%3F\" tabindex=\"-1\">How to reduce bias in AI hiring?<\/h3>\n<p>Here&#8217;s how companies can cut down on bias in AI hiring:<\/p>\n<ol>\n<li>\n<strong>Audit regularly<\/strong>: Check your AI for weird hiring patterns every few months.\n<\/li>\n<li>\n<strong>Use diverse data<\/strong>: Train your AI on a mix of candidate info that matches today&#8217;s workforce.\n<\/li>\n<li>\n<strong>Go blind<\/strong>: Hide names, photos, ages, and gender on resumes. Use IDs instead.\n<\/li>\n<li>\n<strong>Keep humans involved<\/strong>: Have people double-check AI decisions. Let candidates ask for human review.\n<\/li>\n<li>\n<strong>Set clear rules<\/strong>: Define what &quot;fair&quot; means for your company. Have a plan for bias complaints.\n<\/li>\n<\/ol>\n<h3 id=\"does-ai-reduce-bias-in-hiring%3F\" tabindex=\"-1\">Does AI reduce bias in hiring?<\/h3>\n<p>AI can help with some biases, but it&#8217;s not perfect. Here&#8217;s the scoop:<\/p>\n<ul>\n<li><strong>Good<\/strong>: AI spots patterns we miss and ignores irrelevant personal stuff.<\/li>\n<li><strong>Bad<\/strong>: AI learns from human data, so it can pick up and amplify our biases.<\/li>\n<\/ul>\n<p>Take Amazon&#8217;s 2018 AI recruiting tool. It favored men because it learned from 10 years of mostly male hires. They had to scrap it.<\/p>\n<p>To use AI right:<\/p>\n<ul>\n<li>Know its limits<\/li>\n<li>Check for bias often<\/li>\n<li>Use it to support, not replace, human decisions<\/li>\n<\/ul>\n<blockquote>\n<p>&quot;If we take critical steps to address the concerns that are being raised, we can truly harness technology to diversify the workplace.&quot; &#8211; Author Unknown<\/p>\n<\/blockquote>\n<p>Bottom line: AI is powerful, but it needs careful watching to keep hiring fair.<\/p>\n<p><h2>Related posts<\/h2>\n<ul>\n<li><a href=\"\/blog\/5-ways-ai-boosts-linkedin-engagement\" style=\"display: inline;\">5 Ways AI Boosts LinkedIn Engagement<\/a><\/li>\n<li><a href=\"\/blog\/how-to-automate-your-linkedin-presence\" style=\"display: inline;\">How to Automate Your LinkedIn Presence<\/a><\/li>\n<li><a href=\"\/blog\/ultimate-guide-to-linkedin-lead-generation\" style=\"display: inline;\">Ultimate Guide to LinkedIn Lead Generation<\/a><\/li>\n<li><a href=\"\/blog\/ai-vs-traditional-methods-boosting-ad-ctr\" style=\"display: inline;\">AI vs. Traditional Methods: Boosting Ad CTR<\/a><\/li>\n<\/ul>\n<p><script src=\"https:\/\/app.seobotai.com\/banner\/banner.js?id=6713075ad2e8f35dd114ba9b\"><\/script><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Explore 10 effective strategies to reduce AI bias in hiring and ensure fair recruitment practices in the workplace.<\/p>\n","protected":false},"author":2,"featured_media":168,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[5,1],"tags":[],"class_list":["post-169","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-linkedin-marketing","category-uncategorized","eq-blocks"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.7 - 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