{"id":464,"date":"2026-09-16T16:22:37","date_gmt":"2026-09-16T16:22:37","guid":{"rendered":"https:\/\/digest.wiserighteous.org\/?page_id=464"},"modified":"2026-09-16T16:22:37","modified_gmt":"2026-09-16T16:22:37","slug":"sat-style-education-for-righteous-ai","status":"publish","type":"page","link":"https:\/\/digest.wiserighteous.org\/index.php\/sat-style-education-for-righteous-ai\/","title":{"rendered":"SAT Style Education for Righteous AI"},"content":{"rendered":"\n<!DOCTYPE html>\n<html lang=\"en\">\n<head>\n<meta charset=\"UTF-8\">\n<meta name=\"viewport\" content=\"width=device-width, initial-scale=1.0\">\n<title>Section 03 \u00b7 SAT-Style Education \u2014 The Righteousness Digest, Issue 2<\/title>\n<style>\n  :root{\n    --navy:#1a1a2e;\n    --gold:#c9a84c;\n    --gold-soft:#e3c877;\n    --cream:#f5eed6;\n    --cream-dim:#cfc7ae;\n    --red:#e74c3c;\n    --green:#2ecc71;\n    --teal:#16a085;\n    --line:rgba(201,168,76,.28);\n    --line-soft:rgba(201,168,76,.14);\n    --shadow:0 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this browser. You can check your score and export a summary at any time.\n    <\/p>\n  <\/header>\n\n  <nav class=\"tabs\" role=\"tablist\" aria-label=\"SAT skill areas\">\n    <button class=\"tab\" role=\"tab\" id=\"tab-read\" aria-controls=\"panel-read\" aria-selected=\"true\" data-panel=\"panel-read\">\n      <span class=\"dot\"><\/span> Critical Reading\n    <\/button>\n    <button class=\"tab\" role=\"tab\" id=\"tab-data\" aria-controls=\"panel-data\" aria-selected=\"false\" data-panel=\"panel-data\">\n      <span class=\"dot\"><\/span> Data Analysis\n    <\/button>\n    <button class=\"tab\" role=\"tab\" id=\"tab-vocab\" aria-controls=\"panel-vocab\" aria-selected=\"false\" data-panel=\"panel-vocab\">\n      <span class=\"dot\"><\/span> Vocabulary Builder\n    <\/button>\n  <\/nav>\n\n  <main>\n\n    <!-- ============================================================\n         TAB 1: CRITICAL READING\n    ============================================================ -->\n    <section class=\"panel active\" id=\"panel-read\" role=\"tabpanel\" aria-labelledby=\"tab-read\" tabindex=\"0\">\n\n      <div class=\"panel-intro\">\n        <h2>Critical Reading \u2014 &#8220;Two Acts of Conscience&#8221;<\/h2>\n        <p class=\"purpose\">\n          <strong>Skill focus:<\/strong> Identify the central claim, examine supporting evidence, interpret vocabulary\n          in context, and draw inferences from a nonfiction passage. The passage below is adapted from this section&#8217;s\n          article. Read it carefully before answering the questions.\n        <\/p>\n      <\/div>\n\n      <article class=\"passage\">\n        <h3>Two Acts of Conscience<\/h3>\n        <p>\n          <span class=\"lineno\">1<\/span>In 2018, two very different events changed the direction of artificial\n          intelligence. One was a protest inside a technology company. The other was a research paper published by\n          two scientists.\n        <\/p>\n        <p>\n          <span class=\"lineno\">2<\/span>In April 2018, more than 3,000 Google employees signed an open letter\n          demanding that their company cancel its contract with the U.S. Department of Defense for a program called\n          Project Maven. The program used machine learning to analyze drone footage. The employees wrote plainly:\n          &#8220;We believe that Google should not be in the business of war.&#8221; Two months later, Google announced it would\n          not renew the contract.\n        <\/p>\n        <p>\n          <span class=\"lineno\">3<\/span>In the same year, a researcher named Joy Buolamwini and a scientist named\n          Timnit Gebru published a study called &#8220;Gender Shades.&#8221; They tested three commercial facial recognition\n          systems using 1,270 faces from three African and three European countries. The results were unmistakable:\n          all three systems performed worst on darker-skinned women. IBM&#8217;s system had an error rate of 34.7% on\n          darker-skinned women but only 0.3% on lighter-skinned men.\n        <\/p>\n        <p>\n          <span class=\"lineno\">4<\/span>Neither the protest nor the paper was required by any law. No regulator\n          demanded that Google cancel its contract. No court ordered IBM and Microsoft to fix their systems. In both\n          cases, people saw something wrong and said so \u2014 publicly, with evidence, at personal cost.\n        <\/p>\n        <p>\n          <span class=\"lineno\">5<\/span>This is what makes them models of righteousness. They did not wait for\n          permission. They did not wait for the law. They acted on conscience \u2014 and the industry changed because\n          of it.\n        <\/p>\n      <\/article>\n\n      <!-- Q1 -->\n      <article class=\"q\" data-q=\"read-1\" data-answer=\"B\">\n        <div class=\"q-head\">\n          <div class=\"q-num\">1<\/div>\n          <div>\n            <p class=\"q-tag\">Central Ideas and Details<\/p>\n            <p class=\"q-text\">Which choice best states the <strong>central claim<\/strong> of the passage?<\/p>\n          <\/div>\n        <\/div>\n        <ul class=\"choices\">\n          <li class=\"choice\" data-letter=\"A\"><span class=\"letter\">A<\/span><span class=\"text\">Google employees and Joy Buolamwini both worked in the technology industry.<\/span><\/li>\n          <li class=\"choice\" data-letter=\"B\"><span class=\"letter\">B<\/span><span class=\"text\">Acts of individual conscience, rather than regulation, drove two major changes in AI in 2018.<\/span><\/li>\n          <li class=\"choice\" data-letter=\"C\"><span class=\"letter\">C<\/span><span class=\"text\">Facial recognition systems are inherently biased against darker-skinned women.<\/span><\/li>\n          <li class=\"choice\" data-letter=\"D\"><span class=\"letter\">D<\/span><span class=\"text\">Project Maven was more significant than the Gender Shades study.<\/span><\/li>\n        <\/ul>\n        <div class=\"q-actions\">\n          <button class=\"btn-check\" data-check=\"read-1\" disabled>Check Answer<\/button>\n        <\/div>\n        <div class=\"q-feedback\" id=\"fb-read-1\">\n          <p class=\"label\">Explanation<\/p>\n          <p><strong>Correct answer: B.<\/strong> The central argument appears in Paragraph 4: &#8220;Neither the protest nor the paper was required by any law&#8230; people saw something wrong and said so.&#8221; Option A is a factual detail. Option C is a finding, not the central claim. Option D is a comparison the passage never makes.<\/p>\n        <\/div>\n      <\/article>\n\n      <!-- Q2 -->\n      <article class=\"q\" data-q=\"read-2\" data-answer=\"B\">\n        <div class=\"q-head\">\n          <div class=\"q-num\">2<\/div>\n          <div>\n            <p class=\"q-tag\">Command of Evidence<\/p>\n            <p class=\"q-text\">Which detail from the passage <strong>best supports<\/strong> the claim that the Gender Shades study produced measurable, objective findings?<\/p>\n          <\/div>\n        <\/div>\n        <ul class=\"choices\">\n          <li class=\"choice\" data-letter=\"A\"><span class=\"letter\">A<\/span><span class=\"text\">&#8220;They tested three commercial facial recognition systems using 1,270 faces&#8221;<\/span><\/li>\n          <li class=\"choice\" data-letter=\"B\"><span class=\"letter\">B<\/span><span class=\"text\">&#8220;IBM&#8217;s system had an error rate of 34.7% on darker-skinned women but only 0.3% on lighter-skinned men&#8221;<\/span><\/li>\n          <li class=\"choice\" data-letter=\"C\"><span class=\"letter\">C<\/span><span class=\"text\">&#8220;Joy Buolamwini and a scientist named Timnit Gebru published a study&#8221;<\/span><\/li>\n          <li class=\"choice\" data-letter=\"D\"><span class=\"letter\">D<\/span><span class=\"text\">&#8220;No regulator demanded that Google cancel its contract&#8221;<\/span><\/li>\n        <\/ul>\n        <div class=\"q-actions\">\n          <button class=\"btn-check\" data-check=\"read-2\" disabled>Check Answer<\/button>\n        <\/div>\n        <div class=\"q-feedback\" id=\"fb-read-2\">\n          <p class=\"label\">Explanation<\/p>\n          <p><strong>Correct answer: B.<\/strong> This is the specific numerical evidence \u2014 a quantifiable disparity. Option A describes the method, not the finding. Option C identifies the authors. Option D is about Project Maven, not Gender Shades.<\/p>\n        <\/div>\n      <\/article>\n\n      <!-- Q3 -->\n      <article class=\"q\" data-q=\"read-3\" data-answer=\"B\">\n        <div class=\"q-head\">\n          <div class=\"q-num\">3<\/div>\n          <div>\n            <p class=\"q-tag\">Words in Context<\/p>\n            <p class=\"q-text\">As used in Paragraph 5, the word &#8220;<strong>conscience<\/strong>&#8221; most nearly means:<\/p>\n          <\/div>\n        <\/div>\n        <ul class=\"choices\">\n          <li class=\"choice\" data-letter=\"A\"><span class=\"letter\">A<\/span><span class=\"text\">a legal obligation<\/span><\/li>\n          <li class=\"choice\" data-letter=\"B\"><span class=\"letter\">B<\/span><span class=\"text\">an inner sense of what is right and wrong<\/span><\/li>\n          <li class=\"choice\" data-letter=\"C\"><span class=\"letter\">C<\/span><span class=\"text\">a formal complaint<\/span><\/li>\n          <li class=\"choice\" data-letter=\"D\"><span class=\"letter\">D<\/span><span class=\"text\">a written agreement<\/span><\/li>\n        <\/ul>\n        <div class=\"q-actions\">\n          <button class=\"btn-check\" data-check=\"read-3\" disabled>Check Answer<\/button>\n        <\/div>\n        <div class=\"q-feedback\" id=\"fb-read-3\">\n          <p class=\"label\">Explanation<\/p>\n          <p><strong>Correct answer: B.<\/strong> &#8220;Conscience&#8221; in this context refers to the internal moral sense that drives people to act without external compulsion. Option A is the opposite \u2014 the passage explicitly says neither act was required by law.<\/p>\n        <\/div>\n      <\/article>\n\n      <!-- Q4 -->\n      <article class=\"q\" data-q=\"read-4\" data-answer=\"C\">\n        <div class=\"q-head\">\n          <div class=\"q-num\">4<\/div>\n          <div>\n            <p class=\"q-tag\">Inference<\/p>\n            <p class=\"q-text\">Based on the passage, what can be <strong>most reasonably inferred<\/strong> about the relationship between conscience and regulation?<\/p>\n          <\/div>\n        <\/div>\n        <ul class=\"choices\">\n          <li class=\"choice\" data-letter=\"A\"><span class=\"letter\">A<\/span><span class=\"text\">Conscience is a substitute for regulation.<\/span><\/li>\n          <li class=\"choice\" data-letter=\"B\"><span class=\"letter\">B<\/span><span class=\"text\">Regulation is more effective than conscience.<\/span><\/li>\n          <li class=\"choice\" data-letter=\"C\"><span class=\"letter\">C<\/span><span class=\"text\">Conscience can drive change even when regulation does not require it.<\/span><\/li>\n          <li class=\"choice\" data-letter=\"D\"><span class=\"letter\">D<\/span><span class=\"text\">Conscience is only effective in large groups.<\/span><\/li>\n        <\/ul>\n        <div class=\"q-actions\">\n          <button class=\"btn-check\" data-check=\"read-4\" disabled>Check Answer<\/button>\n        <\/div>\n        <div class=\"q-feedback\" id=\"fb-read-4\">\n          <p class=\"label\">Explanation<\/p>\n          <p><strong>Correct answer: C.<\/strong> The passage states that &#8220;neither the protest nor the paper was required by any law&#8221; \u2014 yet &#8220;the industry changed.&#8221; Option A overstates the case. Option B contradicts the passage. Option D contradicts the fact that Joy Buolamwini acted with just one collaborator.<\/p>\n        <\/div>\n      <\/article>\n\n      <!-- Q5 -->\n      <article class=\"q\" data-q=\"read-5\" data-answer=\"B\">\n        <div class=\"q-head\">\n          <div class=\"q-num\">5<\/div>\n          <div>\n            <p class=\"q-tag\">Text Structure and Purpose<\/p>\n            <p class=\"q-text\">What is the <strong>primary rhetorical purpose<\/strong> of Paragraph 4?<\/p>\n          <\/div>\n        <\/div>\n        <ul class=\"choices\">\n          <li class=\"choice\" data-letter=\"A\"><span class=\"letter\">A<\/span><span class=\"text\">To criticize the technology industry for acting too slowly<\/span><\/li>\n          <li class=\"choice\" data-letter=\"B\"><span class=\"letter\">B<\/span><span class=\"text\">To establish that the two events shared a common moral character<\/span><\/li>\n          <li class=\"choice\" data-letter=\"C\"><span class=\"letter\">C<\/span><span class=\"text\">To list the legal requirements that were ignored<\/span><\/li>\n          <li class=\"choice\" data-letter=\"D\"><span class=\"letter\">D<\/span><span class=\"text\">To compare Project Maven with Gender Shades on technical grounds<\/span><\/li>\n        <\/ul>\n        <div class=\"q-actions\">\n          <button class=\"btn-check\" data-check=\"read-5\" disabled>Check Answer<\/button>\n        <\/div>\n        <div class=\"q-feedback\" id=\"fb-read-5\">\n          <p class=\"label\">Explanation<\/p>\n          <p><strong>Correct answer: B.<\/strong> Paragraph 4 explicitly states &#8220;In both cases, people saw something wrong and said so.&#8221; It links the two events. Option C is the opposite \u2014 the passage says there were no legal requirements. Option D is not the purpose of the paragraph.<\/p>\n        <\/div>\n      <\/article>\n\n    <\/section>\n\n    <!-- ============================================================\n         TAB 2: DATA ANALYSIS\n    ============================================================ -->\n    <section class=\"panel\" id=\"panel-data\" role=\"tabpanel\" aria-labelledby=\"tab-data\" tabindex=\"0\">\n\n      <div class=\"panel-intro\">\n        <h2>Data Analysis \u2014 &#8220;The Numbers Behind Gender Shades&#8221;<\/h2>\n        <p class=\"purpose\">\n          <strong>Skill focus:<\/strong> Interpret quantitative information, draw inferences from data, and compare\n          values across categories. Use the table below to answer the questions that follow.\n        <\/p>\n      <\/div>\n\n      <div class=\"data-table-wrap\">\n        <h3>Table 3.4 \u2014 Gender Shades: Error Rates by Skin Tone and Gender<\/h3>\n        <p class=\"table-desc\">\n          Error rates of three commercial facial recognition systems (IBM, Microsoft, Face++) across four demographic\n          groups. Lower percentages indicate better performance. The highest error rates appear in bold.\n        <\/p>\n        <table class=\"data\">\n          <thead>\n            <tr>\n              <th>System<\/th>\n              <th>Lighter Males<\/th>\n              <th>Lighter Females<\/th>\n              <th>Darker Males<\/th>\n              <th>Darker Females<\/th>\n            <\/tr>\n          <\/thead>\n          <tbody>\n            <tr>\n              <td>IBM<\/td>\n              <td class=\"num\">0.3%<\/td>\n              <td class=\"num\">7.1%<\/td>\n              <td class=\"num\">12.0%<\/td>\n              <td class=\"num\"><strong>34.7%<\/strong><\/td>\n            <\/tr>\n            <tr>\n              <td>Microsoft<\/td>\n              <td class=\"num\">0.0%<\/td>\n              <td class=\"num\">4.2%<\/td>\n              <td class=\"num\">8.3%<\/td>\n              <td class=\"num\"><strong>20.8%<\/strong><\/td>\n            <\/tr>\n            <tr>\n              <td>Face++<\/td>\n              <td class=\"num\">0.8%<\/td>\n              <td class=\"num\">5.2%<\/td>\n              <td class=\"num\">13.3%<\/td>\n              <td class=\"num\"><strong>34.5%<\/strong><\/td>\n            <\/tr>\n          <\/tbody>\n        <\/table>\n        <p class=\"table-desc\" style=\"margin-top:14px;margin-bottom:0;\">\n          <em>Source: Buolamwini &amp; Gebru, 2018, Proceedings of Machine Learning Research 81:1\u201315<\/em>\n        <\/p>\n      <\/div>\n\n      <!-- Q6 -->\n      <article class=\"q\" data-q=\"data-1\" data-answer=\"C\">\n        <div class=\"q-head\">\n          <div class=\"q-num\">6<\/div>\n          <div>\n            <p class=\"q-tag\">Quantitative Interpretation<\/p>\n            <p class=\"q-text\">For IBM&#8217;s system, approximately how many times higher was the error rate on darker-skinned females compared with lighter-skinned males?<\/p>\n          <\/div>\n        <\/div>\n        <ul class=\"choices\">\n          <li class=\"choice\" data-letter=\"A\"><span class=\"letter\">A<\/span><span class=\"text\">About 10 times<\/span><\/li>\n          <li class=\"choice\" data-letter=\"B\"><span class=\"letter\">B<\/span><span class=\"text\">About 50 times<\/span><\/li>\n          <li class=\"choice\" data-letter=\"C\"><span class=\"letter\">C<\/span><span class=\"text\">About 115 times<\/span><\/li>\n          <li class=\"choice\" data-letter=\"D\"><span class=\"letter\">D<\/span><span class=\"text\">About 200 times<\/span><\/li>\n        <\/ul>\n        <div class=\"q-actions\">\n          <button class=\"btn-check\" data-check=\"data-1\" disabled>Check Answer<\/button>\n        <\/div>\n        <div class=\"q-feedback\" id=\"fb-data-1\">\n          <p class=\"label\">Explanation<\/p>\n          <p><strong>Correct answer: C.<\/strong> Calculation: 34.7 \u00f7 0.3 \u2248 115.7. The IBM system was over 100 times more likely to misclassify a darker-skinned woman than a lighter-skinned man. Option D is a trap \u2014 it overstates the figure.<\/p>\n        <\/div>\n      <\/article>\n\n      <!-- Q7 -->\n      <article class=\"q\" data-q=\"data-2\" data-answer=\"B\">\n        <div class=\"q-head\">\n          <div class=\"q-num\">7<\/div>\n          <div>\n            <p class=\"q-tag\">Comparative Analysis<\/p>\n            <p class=\"q-text\">Which system performed <strong>best<\/strong> on darker-skinned females?<\/p>\n          <\/div>\n        <\/div>\n        <ul class=\"choices\">\n          <li class=\"choice\" data-letter=\"A\"><span class=\"letter\">A<\/span><span class=\"text\">IBM<\/span><\/li>\n          <li class=\"choice\" data-letter=\"B\"><span class=\"letter\">B<\/span><span class=\"text\">Microsoft<\/span><\/li>\n          <li class=\"choice\" data-letter=\"C\"><span class=\"letter\">C<\/span><span class=\"text\">Face++<\/span><\/li>\n          <li class=\"choice\" data-letter=\"D\"><span class=\"letter\">D<\/span><span class=\"text\">All three performed equally<\/span><\/li>\n        <\/ul>\n        <div class=\"q-actions\">\n          <button class=\"btn-check\" data-check=\"data-2\" disabled>Check Answer<\/button>\n        <\/div>\n        <div class=\"q-feedback\" id=\"fb-data-2\">\n          <p class=\"label\">Explanation<\/p>\n          <p><strong>Correct answer: B.<\/strong> Microsoft&#8217;s error rate on darker-skinned females was 20.8% \u2014 lower than IBM&#8217;s 34.7% and Face++&#8217;s 34.5%. This is a meaningful difference, though still high in absolute terms.<\/p>\n        <\/div>\n      <\/article>\n\n      <!-- Q8 -->\n      <article class=\"q\" data-q=\"data-3\" data-answer=\"C\">\n        <div class=\"q-head\">\n          <div class=\"q-num\">8<\/div>\n          <div>\n            <p class=\"q-tag\">Data Interpretation<\/p>\n            <p class=\"q-text\">What pattern does the table <strong>most clearly<\/strong> suggest?<\/p>\n          <\/div>\n        <\/div>\n        <ul class=\"choices\">\n          <li class=\"choice\" data-letter=\"A\"><span class=\"letter\">A<\/span><span class=\"text\">Error rates decrease as skin tone darkens.<\/span><\/li>\n          <li class=\"choice\" data-letter=\"B\"><span class=\"letter\">B<\/span><span class=\"text\">All systems perform equally across all groups.<\/span><\/li>\n          <li class=\"choice\" data-letter=\"C\"><span class=\"letter\">C<\/span><span class=\"text\">Error rates increase as skin tone darkens, and are highest at the intersection of dark skin and female gender.<\/span><\/li>\n          <li class=\"choice\" data-letter=\"D\"><span class=\"letter\">D<\/span><span class=\"text\">Error rates are determined by the system, not the demographic group.<\/span><\/li>\n        <\/ul>\n        <div class=\"q-actions\">\n          <button class=\"btn-check\" data-check=\"data-3\" disabled>Check Answer<\/button>\n        <\/div>\n        <div class=\"q-feedback\" id=\"fb-data-3\">\n          <p class=\"label\">Explanation<\/p>\n          <p><strong>Correct answer: C.<\/strong> Every system shows the same pattern: lighter males (lowest error), lighter females, darker males, darker females (highest error). The disparity is intersectional \u2014 both skin tone and gender contribute.<\/p>\n        <\/div>\n      <\/article>\n\n      <!-- Q9 -->\n      <article class=\"q\" data-q=\"data-4\" data-answer=\"C\">\n        <div class=\"q-head\">\n          <div class=\"q-num\">9<\/div>\n          <div>\n            <p class=\"q-tag\">Drawing Conclusions<\/p>\n            <p class=\"q-text\">Which conclusion is <strong>best supported<\/strong> by the data?<\/p>\n          <\/div>\n        <\/div>\n        <ul class=\"choices\">\n          <li class=\"choice\" data-letter=\"A\"><span class=\"letter\">A<\/span><span class=\"text\">Facial recognition systems should never be used in any commercial application.<\/span><\/li>\n          <li class=\"choice\" data-letter=\"B\"><span class=\"letter\">B<\/span><span class=\"text\">The disparity was isolated to a single company.<\/span><\/li>\n          <li class=\"choice\" data-letter=\"C\"><span class=\"letter\">C<\/span><span class=\"text\">The disparity was systematic across all three commercial systems tested.<\/span><\/li>\n          <li class=\"choice\" data-letter=\"D\"><span class=\"letter\">D<\/span><span class=\"text\">The systems were poorly designed.<\/span><\/li>\n        <\/ul>\n        <div class=\"q-actions\">\n          <button class=\"btn-check\" data-check=\"data-4\" disabled>Check Answer<\/button>\n        <\/div>\n        <div class=\"q-feedback\" id=\"fb-data-4\">\n          <p class=\"label\">Explanation<\/p>\n          <p><strong>Correct answer: C.<\/strong> All three systems tested showed the same pattern of disparity. Option A is extreme. Option B is contradicted by the data. Option D may be a cause, but is not supported by the table alone.<\/p>\n        <\/div>\n      <\/article>\n\n    <\/section>\n\n    <!-- ============================================================\n         TAB 3: VOCABULARY BUILDER\n    ============================================================ -->\n    <section class=\"panel\" id=\"panel-vocab\" role=\"tabpanel\" aria-labelledby=\"tab-vocab\" tabindex=\"0\">\n\n      <div class=\"panel-intro\">\n        <h2>Vocabulary Builder \u2014 &#8220;Words of Courage and Fairness&#8221;<\/h2>\n        <p class=\"purpose\">\n          <strong>Skill focus:<\/strong> Build academic vocabulary through context, definition, and usage. Each card\n          below presents a key term from the article. Click any card to reveal its definition, pronunciation,\n          example sentence, synonyms, and antonyms. A fill-in-the-blank quiz follows.\n        <\/p>\n      <\/div>\n\n      <div class=\"vocab-grid\" id=\"vocab-grid\">\n\n        <div class=\"vocab-card\" data-word=\"conscience\">\n          <p class=\"vocab-word\">conscience<\/p>\n          <p class=\"vocab-pron\">\/\u02c8k\u0252n.\u0283\u0259ns\/ \u00b7 noun<\/p>\n          <p class=\"vocab-hint\">Click to reveal \u2192<\/p>\n          <div class=\"vocab-detail\">\n            <p class=\"def\">An inner sense of what is right and wrong.<\/p>\n            <div class=\"row\"><span class=\"k\">Example<\/span><span class=\"v\"><em>&#8220;The employees acted on conscience, not instruction.&#8221;<\/em><\/span><\/div>\n            <div class=\"row\"><span class=\"k\">Synonyms<\/span><span class=\"v syn\">moral sense, scruples, ethics<\/span><\/div>\n            <div class=\"row\"><span class=\"k\">Antonyms<\/span><span class=\"v ant\">amorality, indifference<\/span><\/div>\n          <\/div>\n        <\/div>\n\n        <div class=\"vocab-card\" data-word=\"complicity\">\n          <p class=\"vocab-word\">complicity<\/p>\n          <p class=\"vocab-pron\">\/k\u0259m\u02c8pl\u026as.\u0259.ti\/ \u00b7 noun<\/p>\n          <p class=\"vocab-hint\">Click to reveal \u2192<\/p>\n          <div class=\"vocab-detail\">\n            <p class=\"def\">Involvement in wrongdoing, especially by remaining silent.<\/p>\n            <div class=\"row\"><span class=\"k\">Example<\/span><span class=\"v\"><em>&#8220;They refused complicity in the military project.&#8221;<\/em><\/span><\/div>\n            <div class=\"row\"><span class=\"k\">Synonyms<\/span><span class=\"v syn\">collusion, involvement<\/span><\/div>\n            <div class=\"row\"><span class=\"k\">Antonyms<\/span><span class=\"v ant\">innocence, resistance<\/span><\/div>\n          <\/div>\n        <\/div>\n\n        <div class=\"vocab-card\" data-word=\"disparity\">\n          <p class=\"vocab-word\">disparity<\/p>\n          <p class=\"vocab-pron\">\/d\u026a\u02c8sp\u00e6r.\u0259.ti\/ \u00b7 noun<\/p>\n          <p class=\"vocab-hint\">Click to reveal \u2192<\/p>\n          <div class=\"vocab-detail\">\n            <p class=\"def\">A great difference or inequality.<\/p>\n            <div class=\"row\"><span class=\"k\">Example<\/span><span class=\"v\"><em>&#8220;The study showed a large disparity in error rates.&#8221;<\/em><\/span><\/div>\n            <div class=\"row\"><span class=\"k\">Synonyms<\/span><span class=\"v syn\">inequality, gap, imbalance<\/span><\/div>\n            <div class=\"row\"><span class=\"k\">Antonyms<\/span><span class=\"v ant\">equality, parity<\/span><\/div>\n          <\/div>\n        <\/div>\n\n        <div class=\"vocab-card\" data-word=\"benchmark\">\n          <p class=\"vocab-word\">benchmark<\/p>\n          <p class=\"vocab-pron\">\/\u02c8bent\u0283.m\u0251\u02d0k\/ \u00b7 noun<\/p>\n          <p class=\"vocab-hint\">Click to reveal \u2192<\/p>\n          <div class=\"vocab-detail\">\n            <p class=\"def\">A standard used for comparison.<\/p>\n            <div class=\"row\"><span class=\"k\">Example<\/span><span class=\"v\"><em>&#8220;They built a benchmark to test every system fairly.&#8221;<\/em><\/span><\/div>\n            <div class=\"row\"><span class=\"k\">Synonyms<\/span><span class=\"v syn\">standard, yardstick<\/span><\/div>\n            <div class=\"row\"><span class=\"k\">Antonyms<\/span><span class=\"v ant\">\u2014<\/span><\/div>\n          <\/div>\n        <\/div>\n\n        <div class=\"vocab-card\" data-word=\"audit\">\n          <p class=\"vocab-word\">audit<\/p>\n          <p class=\"vocab-pron\">\/\u02c8\u0254\u02d0.d\u026at\/ \u00b7 verb<\/p>\n          <p class=\"vocab-hint\">Click to reveal \u2192<\/p>\n          <div class=\"vocab-detail\">\n            <p class=\"def\">To examine carefully and systematically.<\/p>\n            <div class=\"row\"><span class=\"k\">Example<\/span><span class=\"v\"><em>&#8220;They audited three commercial systems for bias.&#8221;<\/em><\/span><\/div>\n            <div class=\"row\"><span class=\"k\">Synonyms<\/span><span class=\"v syn\">inspect, examine<\/span><\/div>\n            <div class=\"row\"><span class=\"k\">Antonyms<\/span><span class=\"v ant\">ignore, overlook<\/span><\/div>\n          <\/div>\n        <\/div>\n\n        <div class=\"vocab-card\" data-word=\"accountability\">\n          <p class=\"vocab-word\">accountability<\/p>\n          <p class=\"vocab-pron\">\/\u0259\u02ccka\u028an.t\u0259\u02c8b\u026al.\u0259.ti\/ \u00b7 noun<\/p>\n          <p class=\"vocab-hint\">Click to reveal \u2192<\/p>\n          <div class=\"vocab-detail\">\n            <p class=\"def\">The condition of being responsible and answerable.<\/p>\n            <div class=\"row\"><span class=\"k\">Example<\/span><span class=\"v\"><em>&#8220;The paper pushed for algorithmic accountability.&#8221;<\/em><\/span><\/div>\n            <div class=\"row\"><span class=\"k\">Synonyms<\/span><span class=\"v syn\">responsibility, liability<\/span><\/div>\n            <div class=\"row\"><span class=\"k\">Antonyms<\/span><span class=\"v ant\">impunity, immunity<\/span><\/div>\n          <\/div>\n        <\/div>\n\n        <div class=\"vocab-card\" data-word=\"intersectional\">\n          <p class=\"vocab-word\">intersectional<\/p>\n          <p class=\"vocab-pron\">\/\u02cc\u026an.t\u0259\u02c8sek.\u0283\u0259n.\u0259l\/ \u00b7 adjective<\/p>\n          <p class=\"vocab-hint\">Click to reveal \u2192<\/p>\n          <div class=\"vocab-detail\">\n            <p class=\"def\">Relating to overlapping categories, such as race and gender together.<\/p>\n            <div class=\"row\"><span class=\"k\">Example<\/span><span class=\"v\"><em>&#8220;The disparity was intersectional \u2014 it harmed dark-skinned women most.&#8221;<\/em><\/span><\/div>\n            <div class=\"row\"><span class=\"k\">Synonyms<\/span><span class=\"v syn\">overlapping, 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The disparity was <input type=\"text\" data-answer=\"intersectional\" placeholder=\"type here\"> \u2014 it affected dark-skinned women most.<\/span>\n          <span class=\"hint\">(Hint: overlapping categories)<\/span>\n        <\/div>\n        <div class=\"fill-item\">\n          <span class=\"sent\">4. Companies have a duty of <input type=\"text\" data-answer=\"accountability\" placeholder=\"type here\"> to the people their systems affect.<\/span>\n          <span class=\"hint\">(Hint: responsibility, answerability)<\/span>\n        <\/div>\n        <div class=\"fill-item\">\n          <span class=\"sent\">5. Amazon announced a <input type=\"text\" data-answer=\"moratorium\" placeholder=\"type here\"> on police use of its facial recognition system.<\/span>\n          <span class=\"hint\">(Hint: temporary suspension)<\/span>\n        <\/div>\n        <div class=\"fill-item\">\n          <span class=\"sent\">6. 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How to use this module: Work [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-464","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/digest.wiserighteous.org\/index.php\/wp-json\/wp\/v2\/pages\/464","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/digest.wiserighteous.org\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/digest.wiserighteous.org\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/digest.wiserighteous.org\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/digest.wiserighteous.org\/index.php\/wp-json\/wp\/v2\/comments?post=464"}],"version-history":[{"count":1,"href":"https:\/\/digest.wiserighteous.org\/index.php\/wp-json\/wp\/v2\/pages\/464\/revisions"}],"predecessor-version":[{"id":465,"href":"https:\/\/digest.wiserighteous.org\/index.php\/wp-json\/wp\/v2\/pages\/464\/revisions\/465"}],"wp:attachment":[{"href":"https:\/\/digest.wiserighteous.org\/index.php\/wp-json\/wp\/v2\/media?parent=464"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}