[et_pb_section fb_built=”1″ _builder_version=”4.18.0″ _module_preset=”default” background_color=”#FFFFFF” background_enable_image=”off” parallax=”on” global_colors_info=”{}”][et_pb_row admin_label=”Explainer HCAI” _builder_version=”4.18.0″ _module_preset=”default” min_height=”460.1px” custom_padding=”2vw||0vw||false|false” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_text_color=”#000000″ header_text_color=”#000000″ background_layout=”dark” custom_padding=”||0px|||” global_colors_info=”{}”]<\/p>\n
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Human centred analytics is about developing data analytics and AI technologies that lead to a better world. It is about remaining aware that there are human beings represented in the data we work with, and potentially affected by the actions our solutions enable.<\/p>\n
HCAI about considers the humans at every stage of the project, from deciding whether it\u2019s a good idea in the first place, to thinking about how you will deal with unintended consequences (the ones you\u00a0<\/span>weren\u2019t<\/em>\u00a0<\/span>able to anticipate and mitigate).\u00a0It is also about good data and systems hygiene: getting the basics (privacy, informed consent, usability and trustworthiness) right.\u00a0 In two words it\u2019s about being<\/span> ethical <\/span><\/strong>and<\/span> purposeful <\/span><\/strong>in the way we bring new products into the world.<\/span><\/span><\/p>\n You may be surprised by how achievable these goals are. There are technologies that can help improve certain capabilities such as data privacy risk management, but this is predominately about raising awareness and changing perspectives, requiring process refresh and skills development to embed new ways of approaching and governing data science and AI projects.<\/p>\n We work with a network of like minded practitioners in research and industry that bring different experience, tools and perspectives. If you’re working with data and interested in learning more, or have been working in this space for a while and would like to exchange ideas, get in touch, we’d love to hear from you.<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_text_color=”#000000″ header_text_color=”#000000″ custom_margin=”25px||||false|false” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ make_equal=”on” custom_padding_last_edited=”on|desktop” admin_label=”Featured Articles” _builder_version=”4.27.4″ _module_preset=”default” background_color=”RGBA(0,0,0,0)” vertical_offset=”29px” module_alignment=”center” height=”652px” height_tablet=”1329px” height_phone=”1049px” height_last_edited=”on|desktop” custom_margin=”-16px|auto|40px|auto|false|false” custom_margin_tablet=”-16px|auto|40px|auto|false|false” custom_margin_phone=”-16px|auto|40px|auto|false|false” custom_margin_last_edited=”on|desktop” custom_padding=”0vw||5vw||false|false” custom_padding_tablet=”0vw||5vw||false|false” custom_padding_phone=”0vw||5vw||false|false” border_width_all=”1px” border_color_all=”#b2b2b2″ global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_blurb title=”Enabling Diversity and Gender Equity in Human-Centered AI” image=”https:\/\/www.hoc.one\/wp-content\/uploads\/2026\/05\/SM_Image_Horizontal_978-981-95-3658-0.png” _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Ruth Marshall co-authored this chapter in the Handbook of Human-Centered AI (May 2026) with friend and collaborator Theresa Anderson.\u00a0<\/span><\/p>\n <\/span><\/p>\n It offers strategies for exploring the social contexts within which AI systems are intended to be used, building diversity into work teams, and engaging with communities to identify and mitigate potentially detrimental impacts of AI systems. The chapter draws attention to the challenges for putting such strategies in place, with suggestions for future directions. It closes with recommendations for building diversity into teams and applying inclusive practices both in the workplace and when engaging with communities.<\/span><\/p>\n [\/et_pb_blurb][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_blurb title=”What is Human-Centered about Human-Centered AI? A Map of the Research Landscape” url=”https:\/\/dl.acm.org\/doi\/10.1145\/3544548.3580959″ image=”https:\/\/dl.acm.org\/cms\/asset\/f82e6af7-0b5f-44cc-afed-5eead9515297\/3544548.cover.jpg” _builder_version=”4.27.4″ _module_preset=”default” link_option_url=”https:\/\/dl.acm.org\/doi\/10.1145\/3544548.3580959″ global_colors_info=”{}”]<\/p>\n This paper is cited a number of times in our recent chapter on Diversity and Gender Equity in Human Centered AI (adjacent). It provides an overview of the HCAI landscape and provided the perfect mechanism for positioning the focus our discussion in the context if AI “situated in use” (the AI as it interfaces with the world in production) and human values-driven design and management.\u00a0<\/p>\n [\/et_pb_blurb][\/et_pb_column][\/et_pb_row][et_pb_row admin_label=”Resources” _builder_version=”4.18.0″ _module_preset=”default” min_height=”379.8px” custom_margin=”0vw|auto||auto|false|false” custom_padding=”2vw||3vw||false|false” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.18.0″ _module_preset=”default” text_text_color=”#000000″ header_font=”||||||||” header_text_color=”#000000″ background_layout=”dark” min_height=”88.6px” custom_margin=”-35px||||false|false” global_colors_info=”{}”]<\/p>\n Human centered analytics is a widely researched subject with growing industry adoption. This small selection of links will help get you on the learning path if you’re new to the subject. If you have a resource that you think should be here we’d love to hear from you.<\/p>\n [\/et_pb_text][et_pb_tabs _builder_version=”4.18.0″ _module_preset=”default” min_height=”123px” custom_margin=”||||false|false” custom_margin_tablet=”||||false|false” custom_margin_phone=”||6vw||false|false” custom_margin_last_edited=”on|phone” custom_padding=”3vw||3vw||false|false” border_color_all=”#b2b2b2″ global_colors_info=”{}”][et_pb_tab title=”Explainers” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Overview and explainers of human-centerd AI and Analytics.\u00a0<\/p>\n Human-Centred AI: Bridging the gap between ethics and practice (researchfeatures.com)<\/a><\/p>\n Carnegie-Mellon University, National AI Engineering Initiative: Human-centered AI<\/a><\/p>\n Towards Human-Centered Explainable AI: the journey so far (thegradient.pub)<\/a><\/p>\n Human-Centered Artificial Intelligence: Three Fresh Ideas (aisnet.org)<\/a><\/p>\n [\/et_pb_tab][et_pb_tab title=”Standards and Guidelines” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Emerging Australian and international standards and guidelines for human-centric and ethical practice.\u00a0<\/p>\n Australia\u2019s AI Ethics Principles | Department of Industry, Science, Energy and Resources<\/a><\/p>\n EU guidelines on ethics in artificial intelligence: Context and implementation (europa.eu)<\/a><\/p>\n Code of Ethics and Standards | Data Science Professionals | USDSI<\/a><\/p>\n [\/et_pb_tab][et_pb_tab title=”Research Centres” _builder_version=”4.16″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n A selection of research centres around thre world to give you a taste of the different ways the issues are being approached. Note: Hocone is not associated with any of these organisations.\u00a0<\/p>\n Welcome to UTS Human Centred AI (HCAI) Home (hcai-lab.org)<\/a><\/p>\n Human-Centred AI Lab – Human-Centred AI Lab (monash.edu)<\/a><\/p>\n Home | Stanford HAI<\/a><\/p>\n Human-Centered Artificial Intelligence \u2013 HCIL (umd.edu)<\/a><\/p>\nFeatured Articles<\/h1>\n
Resources<\/span><\/h1>\n