Core Viewpoint - The article discusses the concept of "information cocoons" and proposes the idea of "information beehives" as a method to break free from these cocoons, aiming to create a better information ecosystem in the algorithm-driven era [5][34][35]. Group 1: Information Cocoon Concept - The term "information cocoon" was introduced by Cass Sunstein in 2006, highlighting how individuals tend to consume information that aligns with their existing beliefs, leading to a narrow perspective [8][9]. - The article differentiates between "information cocoons," "echo chambers," and "filter bubbles," noting that all three concepts describe how individuals can become isolated in their information consumption [9][11]. - The rise of algorithms has exacerbated the information cocoon phenomenon, as users are increasingly exposed to content that reinforces their existing views, limiting their exposure to diverse perspectives [20][22]. Group 2: Algorithm's Role - Algorithms are designed to maximize user engagement and satisfaction, often leading to a cycle of reinforcing existing interests and preferences [17][18]. - The article identifies four mechanisms of algorithms that contribute to the formation of information cocoons: goal orientation, positive feedback loops, data dependency, and similarity matching [18]. - The transition from a "search for information" model to an "information finds people" model has made it easier for users to access content but has also led to the risk of becoming trapped in echo chambers [19][20]. Group 3: Proposed Solutions - The concept of "information beehives" is introduced as a proactive approach to encourage users to seek diverse information sources and engage with different viewpoints [5][35]. - Recommendations for breaking free from information cocoons include actively subscribing to unfamiliar content, participating in cross-disciplinary discussions, and regularly challenging one's own viewpoints [6][35]. - The article emphasizes the importance of building a collaborative mechanism among content producers, platforms, and consumers to foster a healthier information ecosystem [5][34].

Core Viewpoint - The article highlights the challenges and evolving support systems for delivery workers, emphasizing the balance between work pressure and improved rights and protections in the gig economy [2][4][9]. Group 1: Working Conditions and Challenges - Delivery workers face intense pressure to meet tight deadlines, often leading to unsafe practices and stress [4][5]. - The introduction of algorithms has improved delivery time management, allowing for more flexibility and reduced penalties for delays [5][7]. - Workers have expressed their concerns in forums, leading to policy changes aimed at enhancing their working conditions [4][8]. Group 2: Income and Financial Stability - Many delivery workers initially enter the industry out of necessity, but they find a sense of purpose and financial stability through their work [9][10]. - The implementation of transparent payment systems has allowed workers to track their earnings in real-time, contributing to a sense of security [9][10]. - Some workers have reported significant monthly earnings, which they use to support their families and improve their living conditions [9][10]. Group 3: Social Security and Benefits - There is a disparity in social security coverage among delivery workers, with many not receiving adequate benefits due to the nature of their employment [11][12]. - New policies are being trialed to provide better insurance coverage for gig workers, including occupational injury insurance based on order volume [16][17]. - Some companies are beginning to offer commercial insurance options to supplement workers' benefits, enhancing their financial security [16][17]. Group 4: Community and Support Systems - Local governments and organizations are increasingly involved in supporting delivery workers, providing resources and assistance for their welfare [10][15]. - Initiatives like labor mediation centers and community support programs are being established to address workers' grievances and improve their working conditions [10][15]. - Workers are also engaging in community activities, fostering a sense of belonging and purpose beyond their job roles [10][15].

这场科技与政治的碰撞或许终将改写规则。正如罗斯福新政重塑民主党，里根革命改造共和党，马斯克 带来的不仅是新政党，更是"算法治理""科学决策"等政治范式的颠覆。在气候危机与AI革命的双重夹击 下，美国政治已到不得不变的临界点。无论"美国党"最终成败，马斯克掀起的这场政治实验，都将成为 观测美国制度弹性的重要标本。当星链卫星掠过华盛顿纪念碑上空时，某种新的权力形态正在电离层悄 然酝酿。 然而商业帝国的成功从不是政治游戏的通行证。回顾历史，佩罗特1992年以独立候选人身份拿下19%普 选票却未获一州，布隆伯格2020年豪掷10亿美元竞选仅获3%支持，都印证了第三势力在美国选举人团 制度下的结构性困境。马斯克虽坐拥2.2亿网络粉丝，但要将虚拟世界的声量转化为实体选票，需要构 建覆盖50州的基层组织网络——这远比发射猎鹰火箭更需要持久战的耐心。 两党制百年堡垒的裂缝正在扩大。2024大选后，民主党内部进步派与温和派在医保改革上的公开互撕， 共和党茶党系与传统保守派在移民政策上的水火不容，都为"美国党"提供了战略机遇。马斯克若能联合 硅谷新贵、华尔街改革派和军工复合体中的技术官僚，或可复制19世纪共和党取代辉格党的历史剧 ...

Core Viewpoint - The "Clear Beijing · Intelligent Future" algorithm governance evaluation meeting highlighted the achievements and ongoing efforts in algorithm governance in Beijing, with participation from government departments, internet companies, industry experts, and netizens [1][2]. Group 1: Algorithm Governance Initiatives - The Beijing Internet Information Office, in collaboration with various departments, launched a special action focusing on six typical issues and 27 inspection items related to algorithm governance since November last year [1]. - Major platforms like Douyin, Kuaishou, and Meituan presented their algorithm governance initiatives, including Douyin's verification mechanism for hot topics, Kuaishou's self-assessment and content preference adjustment features, and Meituan's rider engagement meetings and fatigue prevention measures [1]. Group 2: Outcomes and Future Directions - The algorithm governance efforts have shown significant results, but there is a need to focus on weak areas to enhance algorithm safety comprehensively [2]. - The Beijing Internet Information Office plans to further research and implement effective measures for algorithm governance, aiming to transition from concentrated governance to a more normalized governance approach [2].

转自：北京日报客户端 北京市网信办、首都互联网协会昨天共同召开"清朗京华·智算未来"首都算法治理成果评议会，政府管 理部门、互联网企业、行业专家及网民四方代表参会。去年11月以来，北京市网信办联合市公安局、市 市场监管局、市通信管理局等部门，开展"清朗·网络平台算法典型问题治理"专项行动，聚焦6类典型问 题27个检查项进行专项治理。为持续提升属地网站平台算法典型问题治理能力，分享交流先进治理经 验，集中展示阶段性工作成果。 会上，抖音、快手、美团先后作交流发言。抖音推出热点当事人核实机制，利用算法提升热点事件 中"自媒体"账号传播虚假信息误导公众、假冒仿冒当事人等问题识别处置能力。快手推出"信息茧房"自 我评估功能和"一键破茧"功能，强化用户赋权，支持内容偏好自主调节，有效回应网民反映强烈的短视 频信息茧房问题。美团先后组织召开100余场骑手恳谈会，推出超时免罚和8小时预警、12小时强制休息 机制，用算法和规则实现管理温度和社会责任的统一。参会的30家网站平台均结合平台特色属性开展算 法创新治理，取得了一定成效。 美团外卖骑手孙喜贺表示，美团在算法专项治理期间的各项举措切实改善了骑手的工作体验，如"等灯 等灯 ...

平台经济凭借其就业形态创新、灵活用工机制和普惠性岗位供给能力，成为稳就业关键载体。当前，如 何更好发挥平台经济在促就业上的重要作用，值得关注。 不过，一些问题也不容忽视。例如，一些企业通过协议嵌套方式规避用工责任，将劳动者单方面界定 为"个体工商户"或"合作伙伴"，导致其工伤保险、失业救济等法定权益实质性缺失。数据显示，截至 2024年，全国外卖骑手、网约车司机群体中纳入城镇职工社保体系比例不足四成，养老保险断缴率超 40%，相关保障缺位已成为制约平台经济持续吸纳就业的重要因素。又如，外卖平台通过算法"优化"压 缩配送时效，造成骑手交通违章率较高；网约车平台利用"高峰期加价算法"转移经营风险，导致司机高 峰时段收入难以覆盖车辆运营成本；直播行业阶梯式抽成机制叠加算法流量倾斜，持续挤压新人主播生 存空间。为有效解决这些问题，还需多措并举。 商务部数据显示，2023年以来，平台经济年均新增就业岗位占全国比重稳定在22%以上，已累计吸纳超 2.3亿人就业。同时，平台经济加速推动人才结构向高端化演进。据人社部统计，截至2024年，全国人 工智能工程技术人员突破120万人，近3年复合增长率达45%，其中AI训练师、区块 ...

打开短视频平台，哗众取宠的内容似乎总刷不完；浏览社交软件，每一条推送似乎都是"量身定制"…… 算法推荐技术编织的"信息茧房"和低俗内容陷阱，困扰着不少网民。针对这些算法推荐乱象，中央网信 办督导重点平台进行整改，取得了一定效果。近日，一些重点平台系统性优化完善多项功能。但算法失 范的深层治理，远非一日之功。 算法推荐技术已深度融入经济社会发展的各个领域和场景。它通过抓取用户日常使用数据，分析用户行 为、习惯和偏好，从而精准提供信息、娱乐、消费等各类服务。可以说，算法推荐在很大程度上帮助用 户过滤掉冗余信息，提高了信息获取效率，解决了信息过载问题，成为平台吸引高黏性用户、驱动增长 的重要方式。 但作为一种技术工具，算法推荐也是一把"双刃剑"，一旦运用失范，不可避免地会产生一些乱象。首要 问题是同质化内容过多，形成"信息茧房"。算法推荐以用户偏好为导向，持续推送大量同类内容，将用 户隔绝在一个封闭的信息空间中，阻碍了多样信息流动，长此以往可能使用户对真实复杂世界的认知出 现偏差，加剧观点极化。 低俗信息现象也不容轻视。算法推荐往往遵循"流量至上"的价值取向，一些猎奇、肤浅、低俗的内容往 往成为算法推荐的"宠儿"。 ...

Core Insights - Ele.me has conducted its fourth key algorithm disclosure this year, focusing on transparency and rider rights protection [1][3] - The company has established an expert committee and initiated monthly rider forums to enhance communication and feedback mechanisms [1][4] - The optimization of algorithms aims to improve rider efficiency and income, with a current order matching success rate of 89% [2][3] Algorithm Optimization - The four key algorithms disclosed include order dispatch, delivery pricing, delivery time estimation, and route planning [1][3] - Continuous improvements are being made in dispatch rules, delivery time accuracy, and pricing adjustments for difficult orders [3][4] - Collaboration with high-precision mapping services has reduced average store search time by 17% and user search time by 11% [2] Rider Support and Feedback - Ele.me has introduced an AI assistant named "Xiao E" to enhance rider efficiency and experience [4] - A feedback mechanism has been established to address issues such as indoor routing challenges and road closures [2][4] - The importance of fairness, transparency, and trust in algorithm governance has been emphasized by experts during the forums [4]

持续算法公开，加强多元化沟通，饿了么一直在行动。记者获悉，近期联合中国新就业研究中心举行的 蓝骑士恳谈会上，饿了么公开路线规划算法，响应骑手在出行方面的需求和反馈，让骑手送单更精准、 更顺路、更省心。 已公开四项关键算法，缓解骑手跑单焦虑 这次蓝骑士恳谈会，已是今年以来饿了么进行的第四次关键算法公开，前三次分别为"派单调度算 法""配送计价算法"和"配送时间预估算法"。 自今年初宣布推出九项举措，推动算法透明化与骑手权益保障以来，饿了么陆续成立即时配送算法专家 委员会，推出"骑手权益月恳谈会"创新机制。目前，在北京、上海、深圳多地举行的系列恳谈会上，已 公开"派单调度""配送计价""配送时间预估"和"路线规划"四项关键算法，通过难取难送优化、距离定位 更精准、新骑手保护、难单定价更合理、顺路率提升等系列优化举措，为骑手构建更完善的权益保障体 系。 算法持续优化升级，合单顺路率达89% 据饿了么调研显示，对于配送，骑手的top5关注点包括商家出餐速度、车子能否进小区、订单是否顺 路、电梯等待时间、是否需要绕路等等。这背后，外卖的"接取送达"都离不开路线规划。 "相对于通用地图的路线规划，外卖配送有鲜明的独特性。 ...

中新网北京5月30日电 (记者 夏宾)近年来，国家陆续出台了《关于维护新就业形态劳动者劳动保障权益 的指导意见》《互联网信息服务算法推荐管理规定》等政策文件，关注平台企业的新就业形态劳动者权 益保护。 中国物流与采购联合会网络事业部主任、物流信息服务平台分会秘书长晏庆华近日在北京出席互联网货 运行业算法研讨会时称，上述文件明确提出算法取中原则，要求平台企业优化算法规则，保障劳动者权 益，要推动算法向善向上，保障新就业群体权益问题，不能仅靠政府监管或企业自律，需要构建政府、 行业协会、平台、工会、劳动者等多方共同参与的体系，实现共同参与协同治理。 为深化多元共治，实现行业共融，晏庆华建议，强化政府统筹，完善政策法规，建议政府出台算法治理 专项法规，明确各方权责边界。发挥工会作用，构建协商平台，工会可探索建立与平台企业的常态化协 商机制，将算法优化、权益保障等纳入协商范围，并积极吸纳新就业形态劳动者入会，通过集体协商， 参与算法审计等方式维护权益。 同时，发挥协会作用，促进行业自律。平台应积极参与行业学会组织的活动，签署自律公约，承诺在算 法设计和应用中遵循公平、公正、透明的原则，行业协会可制定算法透明化和向善向 ...